INFORMATION

CROSS-REFERENCE

[0001] This application claims priority to U.S. Provisional Patent Application Serial No. 63/180,822 filed April 28, 2021, which is incorporated herein by reference in its entirety for all purposes.

TECHNICAL FIELD

[0002] The present disclosure generally relates to systems and methods for providing and analyzing health information. More particularly, the present disclosure relates to systems and methods designed to retain health information and provide an analysis on data elements at a remote device.

BACKGROUND

[0003] Biotechnological and pharmaceutical companies rely on clinical evidence from clinical trials to develop and market drugs. Estimates have put the medical cost of conducting pivotal trials at approximately $19 million. In addition to this high cost of clinical trials, the protocols utilized by each clinical trial do not align within a clinical practice and/or a patient group is not homogenous. Because a majority of patient data is recorded on hard-copy medical records, access to evidence from clinical trials is difficult to locate and access.

[0004] Conventional solutions have attempted to address these problems by uploading and recording patient records into a computer database. Additionally, Real World Evidence (RWE) is used in place of clinical trial data that is generally cheaper and represents real world clinical practice across a heterogeneous group of patients. The data from RWE is currently sorted into various databases depending on the type of patient data. Although storing patient data into several databases may prevent loss of patient data, having data spread across various databases makes it difficult to access data and generate insights about the data. Additionally, data privacy laws relating to personal data protection make it difficult for companies to view patient records without accidentally acquiring personal information about the patient ( e.g name, address, phone number, etc.), preventing access to critical data in order to comply with these data privacy laws. [0005] Specifically, in the context of behavioral health, there is a lack of commercially available Real World Data (RWD). Additionally, data generated in behavioral health clinics is distinct from other medical specialties making analytics using this data uniquely difficult. For instance, behavioral health data often has a reliance on subjective scales and/or questionnaires to diagnose and assess severity of a disease, as compared to measurable biomarkers in other disease areas.

[0006] Conventional solutions to these problems fail to offer data that is not normalized or purged of personally identifiable information (PII). The few anonymized datasets that are available is by users who upload their own normalized and anonymized data, which can include human error and incorporate inaccuracies. Additionally, conventional solutions perform standard analytics, creating a need for enabling performance of customizable analytics on the data.

[0007] Given the above background, there is a need in the art for improved systems and methods for retaining health information and providing an analysis on data elements at a remote device.

[0008] The information disclosed in this Background of the Invention is only for the enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

SUMMARY

[0009] Advantageously, the systems and methods detailed in the present disclosure address the shortcomings in the prior art detailed above.

[0010] Systems and methods for retaining health information are provided. Specifically, the present disclosure facilitates retaining health information and providing an analysis on the health information. A plurality of data elements is received that includes a plurality of health records associated with a plurality of patients. Typically, the plurality of data elements is received in a plurality of formats, such as using different units of measurement or mixing different medical codes to describe a similar type of health information, and often includes unreliable or unnecessary health information. Accordingly, the present disclosure normalizes the plurality of data elements by not only formatting each respective data element in the plurality of data elements, but also forming a temporal data element associated with the respective data element. From this, a plurality of normalized data elements is formed that provides a uniform template when analyzing the health information associated with the data elements. Moreover, by forming the temporal association, an analysis of the normalized data elements is capable of using one or more time-dependent computational tools on one or more data elements that previously lacked temporal characteristics and/or a time-independent computational tool on one or more data elements that previously required a temporal characteristics. Additionally, by controlling the normalizing and retaining of the plurality of data elements, the analysis of the normalize data elements is capable of being conducted at any scale of granularity.

[0011] One aspect of the present disclosure provides systems for retaining health information. Specifically, a computer system includes at least one processor and a memory storing at least one program. The at least one program is configured for execution by the at least one processor.

The at least one program includes instructions for receiving, in electronic form, a plurality of data elements from a remote device. The plurality of data elements includes a plurality of health records associated with a plurality of patients. Each respective health record in the plurality of health records includes a characteristic in a plurality of characteristics of a corresponding patient in the plurality of patients. The program further includes instructions for normalizing the plurality of data elements. The normalizing forms a plurality of normalized data elements. Moreover, the normalizing includes, for each respective data element in the plurality of data elements, formatting the respective data element from a first format associated with a source of a corresponding health record to a predetermined format. Additionally, the normalizing includes forming a temporal data element associated with the respective data element for each respective data element in the plurality of data elements. The at least one program additionally includes instructions for retaining, in the memory, the plurality of normalized data elements. In this way, the computer system retains the information.

[0012] In some embodiments, the formatting of the normalizing further includes determining if a respective health record in the plurality of health records is associated with a respective measurement in a plurality of measurements. Moreover, the formatting includes, in accordance with a determination that the respective health record is associated with the respective measurement, formatting the respective health record from the first format to the predetermined format.

[0013] In some embodiments, the first format is an electronic medical record format.

[0014] In some embodiments, the predetermined format is a quantitative format.

[0015] In some embodiments, the quantitative format is a unit of measurement.

[0016] In some embodiments, the plurality of characteristics includes a plurality of qualitative characteristics, a plurality of quantitative characteristics, or both.

[0017] In some embodiments, the plurality of qualitative characteristics includes a first qualitative characteristic associated with a diagnosis of a medical condition.

[0018] In some embodiments, the plurality of qualitative characteristics includes a second qualitative characteristic associated with a treatment of a medical condition.

[0019] In some embodiments, the plurality of qualitative characteristics includes a third qualitative characteristic associated with a demographic of a respective patient in the plurality of patients.

[0020] In some embodiments, the plurality of quantitative characteristics includes a plurality of quantitative temporal characteristics, a plurality of quantitative measurement characteristic, or both.

[0021] Another aspect of the present disclosure is directed to providing a method of analyzing information. The method includes receiving, in electronic form, a request for an analysis of a candidate subject from a remote device. The method further includes retrieving, in response to the request, a plurality of data elements associated with the candidate subject from a database. Additionally, the method includes receiving, based on the retrieving, a selection of a respective tool in a plurality of tools from the remote device. The plurality of tools includes a plurality of computational tools and a plurality of data source tools. Each respective computational tool in the plurality of computational tools utilizes a unique data processing model in a plurality of data processing models. Furthermore, each respective data source tool in the plurality of data source tools is associated with a corresponding characteristic in a plurality of characteristics. In addition, the method includes providing, based on the selection of the respective tool, an analysis of the plurality of data elements to the remote device.

[0022] In some embodiments, the plurality of data processing models includes a plurality of statistical analysis models, a plurality of time-based models, a plurality of machine learning models, a plurality of user-defined models, or a combination thereof.

[0023] In some embodiments, the plurality of statistical analysis models includes a plurality of correlation models, a plurality of comparison models, a plurality of regression models, a plurality of classification models, a plurality of survival analysis models, a plurality of product limit estimation models, a plurality of ranking models, a plurality of cox proportional hazard models, or a combination thereof.

[0024] In some embodiments, the plurality of correlation models includes a continuous variable correlation model, an ordinal correlation model, or both.

[0025] In some embodiments, the plurality of comparison models includes one or more comparison of means models.

[0026] In some embodiments, the one or more comparison of means models includes a Z-test model, a paired T-test model, an independent T-test model, a Chi-square test model, an analysis of variance model, or a combination thereof.

[0027] In some embodiments, the plurality of regression models includes a linear regression model. [0028] In some embodiments, the plurality of classifications models includes a logistic regression model, a score classification model, or both.

[0029] In some embodiments, the score classification model includes a contingency table for logistic regression score model, an area under a receiver operating characteristic curve score model, an FI score model, a Brier score loss model, a specificity score model, a sensitivity score model, a prevalence score model, a true positive rate score model, a false positive rate score model, a positive predictive value score model, a negative predictive value score model, or a combination thereof.

[0030] In some embodiments, the plurality of time-based models includes one or more linear fixed effect models, one or more linear random effect models, one or more time to event models, one or more exposure-effect correlation models, or a combination thereof.

[0031] In some embodiments, the plurality of machine learning models includes one or more random forest models, one or more random survival forest models, one or more extreme gradient boosting models, one or more support vector machine models, one or more Gaussian mixture models, one or more neural network models, or a combination thereof.

[0032] In some embodiments, the plurality of user-defined models includes one or more user- defined logic gate models, one or more user-defined data formatting models, one or more user- defined derivation models, or a combination thereof.

[0033] In some embodiments, the plurality of computational tools includes a plurality of visualization computational tools.

[0034] In some embodiments, the plurality of visualization computational tools includes one or more graphical chart visualization tools, one or more table visualization tools, one or more diagram visualization tools, or a combination thereof.

[0035] In some embodiments, the providing further includes displaying, on a display of the remote device, a visualization of the analysis of the plurality of data elements based on a determination that the respective tool in the plurality of tools selected of the selecting (C) is a corresponding visualization tool in the plurality of visualization tools.

[0036] In some embodiments, the plurality of characteristics includes a plurality of qualitative characteristics, a plurality of quantitative characteristics, or both.

[0037] In some embodiments, the plurality of characteristics includes a characteristic associated with an age, a gender, an ethnicity, a weight, a height, a diagnostic information, a pharmaceutical composition information, a treatment information, a site identification, a baseline year, physical behavioral information, emotional behavioral information, mental information, visit type, severity of illness scale, a severity of functioning scale, a social history, a stressful and/or benevolent life event, information about one or more side effects from administering a pharmaceutical composition, information about substance abuse, information about a history of illnesses of close family members, or a combination thereof.

[0038] Other features and advantages of the invention will be apparent from, or are set forth in more detail in, the accompanying drawings, which are incorporated herein, and the following Detailed Description, which together serve to explain certain principles of exemplary embodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS [0039] Figure 1 illustrates an exemplary system topology including an analysis system, in accordance with an embodiment of the present disclosure;

[0040] Figure 2 illustrates various modules and/or components of an analysis system, in accordance with an embodiment of the present disclosure;

[0041] Figure 3 illustrates various modules and/or components a remote device, in accordance with an embodiment of the present disclosure;

[0042] Figures 4A and 4B collectively provide a flow chart of methods for retaining health information, in accordance with an embodiment of the present disclosure;

[0043] Figures 5A, 5B, and 5C collectively provide a flow chart for methods for analyzing health information, in accordance with an embodiment of the present disclosure;

[0044] Figure 6 illustrates a graphical user interface (GUI) for retaining and analyzing health information, in accordance with an embodiment of the present disclosure;

[0045] Figure 7 illustrates a GUI for selecting a plurality of tools for analyzing health information, in accordance with an embodiment of the present disclosure;

[0046] Figure 8 illustrates another GUI for selecting a plurality of tools for analyzing health information, in accordance with an embodiment of the present disclosure;

[0047] Figure 9 illustrates yet another GUI for selecting a plurality of tools for analyzing health information, in accordance with an embodiment of the present disclosure;

[0048] Figure 10 illustrates a GUI for conducting an analysis of health information, in accordance with an embodiment of the present disclosure;

[0049] Figure 11 illustrates another GUI for conducting an analysis of health information, in accordance with an embodiment of the present disclosure;

[0050] Figure 12 illustrates a GUI for visualizing retained health information, in accordance with an embodiment of the present disclosure; and

[0051] Figure 13 illustrates yet another GUI for selecting a plurality of tools for analyzing health information, in accordance with an embodiment of the present disclosure.

[0052] In the figures, reference numbers refer to the same or equivalent parts of the present invention throughout the several figures of the drawing.

DETAILED DESCRIPTION

[0053] The present description relates to systems and methods for retaining and analyzing health information. Specifically, the methods include receiving, in electronic form, a plurality of data elements from a remote device. The plurality of data elements includes a plurality of health records associated with a plurality of patients. Furthermore, each respective health record in the plurality of health records includes a characteristic in a plurality of characteristics of a corresponding patient in the plurality of patients. Additionally, the methods include normalizing the plurality of data elements, thereby forming a plurality of normalized data elements. In some embodiments, the normalizing includes, for each respective data element in the plurality of data elements, formatting the respective data element from a first format associated with a source of a corresponding health record to a predetermined format. In some embodiments, the normalizing includes, for each respective data element in the plurality of data elements, forming a temporal data element associated with the respective data element. The method further includes retaining, in the memory, the plurality of normalized data elements, thereby retaining the information. [0054] Reference will now be made in detail to various embodiments of the present invention(s), examples of which are illustrated in the accompanying drawings and described below. While the invention(s) will be described in conjunction with exemplary embodiments, it will be understood that the present description is not intended to limit the invention(s) to those exemplary embodiments. On the contrary, the invention(s) is/are intended to cover not only the exemplary embodiments, but also various alternatives, modifications, equivalents and other embodiments, which may be included within the spirit and scope of the invention as defined by the appended claims.

[0055] It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For instance, a first candidate subject could be termed a second candidate subject, and, similarly, a second candidate subject could be termed a first candidate subject, without departing from the scope of the present disclosure. The first candidate subject and the candidate subject are both candidate subjects, but they are not the same candidate subject. [0056] The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes” and/or “including,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

[0057] The foregoing description included example systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative implementations. For purposes of explanation, numerous specific details are set forth in order to provide an understanding of various implementations of the inventive subject matter. It will be evident, however, to those skilled in the art that implementations of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures and techniques have not been shown in detail.

[0058] The foregoing description, for purpose of explanation, has been described with reference to specific implementations. However, the illustrative discussions below are not intended to be exhaustive or to limit the implementations to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The implementations are chosen and described in order to best explain the principles and their practical applications, to thereby enable others skilled in the art to best utilize the implementations and various implementations with various modifications as are suited to the particular use contemplated.

[0059] In the interest of clarity, not all of the routine features of the implementations described herein are shown and described. It will be appreciated that, in the development of any such actual implementation, numerous implementation-specific decisions are made in order to achieve the designer’s specific goals, such as compliance with use case- and business-related constraints, and that these specific goals will vary from one implementation to another and from one designer to another. Moreover, it will be appreciated that such a design effort might be complex and time-consuming, but nevertheless be a routine undertaking of engineering for those of ordering skill in the art having the benefit of the present disclosure.

[0060] As used herein, the term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.

[0061] As used herein, the term “about” or “approximately” can mean within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which can depend in part on how the value is measured or determined, e.g., the limitations of the measurement system. For example, “about” can mean within 1 or more than 1 standard deviation, per the practice in the art. “About” can mean a range of ± 20%, ± 10%, ± 5%, or ± 1% of a given value. Where particular values are described in the application and claims, unless otherwise stated, the term “about” means within an acceptable error range for the particular value. The term “about” can have the meaning as commonly understood by one of ordinary skill in the art. The term “about” can refer to ± 10%. The term “about” can refer to ± 5%.

[0062] As used herein, the term “dynamically” means an ability to update a program while the program is currently running.

[0063] Additionally, the terms “client,” “subject,” and “user” are used interchangeably herein unless expressly stated otherwise.

[0064] Furthermore, when a reference number is given an “z ¹¹¹ ” denotation, the reference number refers to a generic component, set, or embodiment. For instance, a characteristic termed “characteristic z” refers to the z ^'th characteristic in a plurality of communications (e.g., a first characteristic 224-1 in a plurality of characteristics 224).

[0065] In the present disclosure, unless expressly stated otherwise, descriptions of devices and systems will include implementations of one or more computers. For instance, and for purposes of illustration in Figure 1, a client device 300 is represented as single device that includes all the functionality of the client device 300. However, the present disclosure is not limited thereto.

For instance, the functionality of the client device 300 may be spread across any number of networked computers and/or reside on each of several networked computers and/or by hosted on one or more virtual machines at a remote location accessible across a communications network (e.g., communications network 114). One of skill in the art will appreciate that a wide array of different computer topologies is possible for the client device 300, and other devices and systems of the preset disclosure, and that all such topologies are within the scope of the present disclosure.

[0066] Figure 1 illustrates an exemplary topography of a computing system 100 for retaining health information and/or conducting an analysis of heath information. The computing system 100 includes an analysis system ( e.g ., analysis system 200 of Figure 2) that receives a communication over a communication network(s) 114. One or more client devices (e.g., first client device 300-1 of Figure 3) communicate with the analysis system 200 through the communication network 114, such as communicating a request to conduct an analysis on the retained health information. Each client device 300 is associated with at least one user (e.g., a first client device 300-1 is associated with a first user, a second client device 300-2 is associated with a second user, a third client device 300-3 associated with a third user and a fourth user, etc.).

[0067] A detailed description of a computing system 100 for retaining health information and/or analyzing the retained health information in accordance with the present disclosure is described in conjunction with Figure 1 through Figure 3. As such, Figure 1 through Figure 3 collectively illustrate an exemplary topology of the computing system 100 in accordance embodiments of the present disclosure. The system 100 includes an analysis system 200 for receiving a plurality of data elements that include a plurality of health records (e.g., data elements including health information for one or more patients), conducting an analysis the data elements, retaining the data elements such that health information of the heath records are stored by the computing system 100, or a combination thereof. The analysis system 200 utilizes one or more modules and/or components (e.g., formatting module 222 of Figure 2; analysis module 226 of Figure 2; etc.) to analyze patient health information of the data elements.

[0068] Referring to Figure 1, the analysis system 200 is configured formatted and/or conduct an analysis on a plurality of data elements that includes health information for one or more patients in the form of a plurality of health records. In some embodiments, the analysis system 200 receives a data element across a communication network 114 from a remote device (e.g., client device 300), such as a database (e.g., a data store, a data lake, etc.) and/or a server remote to the analysis system 200 and/or the computing system 100. In this way, the data element is provided in electronic form to the analysis system 200 (e.g., in an electronic unformatted structured format, in an electronic structured format, or a combination thereof).

[0069] In some embodiments, the analysis system 200 receives a data element wirelessly through radio-frequency (RF) signals. In some embodiments, such signals are in accordance with an 802.11 (Wi-Fi), Bluetooth, orZigBee standard.

[0070] In some embodiments, the analysis system 200 receives a data element directly from a respective source (e.g., directly from a first client device 300-1). For instance, in some embodiments, a respective client device 300 is associated with a corresponding medical practitioner and/or clinic that treats one or more patients, such that the corresponding medical practitioner and/or clinic can communicate a plurality of data elements that includes one or more health records ( e.g ., an electronic medical record (EMR)) associated with the one or more patients of the corresponding medical practitioner and/or clinic. However, the present disclosure is not limited thereto. For instance, in some embodiments, a first client device 300-1 communicates a plurality of data element to the analysis system 200 that includes a first health record of a first user associated with the first client device 300. In some embodiments, the analysis system 200 receives a communication from a remote device, such as an auxiliary server (e.g., from a remote application host server).

[0071] In some embodiments, the analysis system 200 is not proximate to a user and/or does not have wireless capabilities or such wireless capabilities are not used for the purpose of receiving a data element. In such embodiments, the communication network 114 is utilized to receive a data element from a source (e.g., first client device 300-1, second client device 300-2, . . ., 300-R) to the analysis system 200.

[0072] In some embodiments, the analysis system 200 receives at least 10 data elements in the plurality of data elements, at least 20 data elements in the plurality of data elements, at least 30 data elements in the plurality of data elements, at least 40 data elements in the plurality of data elements (e.g., 42 data elements), at least 50 data elements in the plurality of data elements, at least 60 data elements in the plurality of data elements, at least 70 data elements in the plurality of data elements, at least 80 data elements in the plurality of data elements, at least 90 data elements in the plurality of data elements, at least 100 data elements in the plurality of data elements, at least 200 data elements in the plurality of data elements, at least 300 data elements in the plurality of data elements, at least 400 data elements in the plurality of data elements, at least 500 data elements in the plurality of data elements, at least 1,000 data elements in the plurality of data elements, or at least 5,000 data elements in the plurality of data elements. In some embodiments, the plurality of data elements received by the analysis system 200 includes a range of data elements from about 1 data element to about 10 data elements, from about 5 data elements to about 25 data elements, from about 20 data elements to about 45 data elements, from about 25 data elements to about 60 data elements, from about 30 data elements to about 90 data elements, from about 50 data elements to about 100 data elements, from about 50 data elements to about 200 data elements, from about 100 data elements to about 200 data elements, from about 100 data elements to about 500 data elements, from about 250 data elements to about 1,000 data elements, from about 250 data elements to about 2,500 data elements, from about 500 data elements to about 5,000 data elements, from about 1,000 data elements to about 10,000 data elements, or a combination thereof. [0073] Examples of communication networks 114 include, but are not limited to, the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The wireless communication optionally uses any of a plurality of communications standards, protocols and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W- CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) ( e.g ., IEEE 802.11a, IEEE 802.1 lac, IEEE 802.1 lax, IEEE 802.1 lb, IEEE 802.1 lg and/or IEEE 802.1 In), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for e-mail ( e.g ., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging ( e.g ., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of the present disclosure.

[0074] Of course, other topologies of the computing system 100 other than the one depicted in Figure 1 are possible. For instance, in some embodiments, rather than relying on a communications network 114, the one or more client devices 300 wirelessly transmit information directly to the analysis system 200. Further, in some embodiments, the analysis system 200 and/or the client device 300 constitutes a portable electronic device, a server computer, or in fact constitutes several computers that are linked together in a network, or be a virtual machine and/or a container in a cloud-computing context. As such, the exemplary topology shown in Figure 1 merely serves to describe the features of an embodiment of the present disclosure in a manner that will be readily understood to one of skill in the art.

[0075] Turning to Figure 2 with the foregoing in mind, in some embodiments, the analysis system 200 includes one or more computers. For purposes of illustration in Figure 2, the analysis system 200 is represented as a single computer that includes all of the functionality for retaining and analyzing health information. However, the present disclosure is not limited thereto. In some embodiments, the functionality for providing an analysis system 200 is spread across any number of networked computers, and/or resides on each of several networked computers, and/or is hosted on one or more virtual machines and/or one or more containers at a remote location accessible across the communication network 114. One of skill in the art will appreciate that any of a wide array of different computer topologies are used for the application and all such topologies are within the scope of the present disclosure.

[0076] An exemplary analysis system 200 for retaining and/or analyzing heath information of data elements is provided. The analysis system 200 includes one or more processing units (CPU’s) 202, a network interface 204 or other communications interface, a memory 212 ( e.g ., random access memory), and one or more communication busses 214 for interconnecting the aforementioned components. In some embodiments, the analysis system 200 includes a user interface 206 for visualizing and interacting with the aforementioned components and/or the communication network 114. In some embodiments, the user interface 206 includes a display 208 and an input 210 ( e.g ., keyboard, keypad, touch screen, etc.). In some embodiments, the memory 212 includes mass storage that is remotely located with respect to the central processing unit(s) 202. In other words, some data stored in the memory 212 may in fact be hosted on computers that are external to the analysis system 200, but that can be electronically accessed by the analysis system 200 over an Internet, intranet, or other form of network or electronic cable (illustrated as element 114 in Figure 2) using network interface 204.

[0077] In some embodiments, the memory 212 of the analysis system 200 for retaining health information and/or conducting an analysis on the health information includes:

• an operating system 216 that includes procedures for handling various basic system services;

• an electronic address 218 that is associated with the analysis system 200;

• a formatting module 220 that includes a plurality of formats (e.g., first format 222-1, second format 222-2, . . ., format L 222 -L, etc.), each respective format 222 including a plurality of characteristics (e.g., first characteristic 224-1, second characteristic 224-2, . .

., characteristic J 222-/, etc.) that is particular to the respective format 222; and

• an analysis module 226 that includes a tool module 228 for providing various mechanisms for conducting an analysis of health information including a computation tool module 230 having one or more computational tools 232 (e.g., first computational tool 232-1, second computational tool 232-2, . . ., computational tool M 232 -M, etc.) and a data source tool module 234 having one or more data sources tools 236 (e.g., first data source tool 236-1, second data source tool 236-2, . . . , data source tool N 232 -M, etc.).

[0078] As illustrated in Figure 2, the analysis system 200 preferably includes an operating system 216 that includes procedures for handling various basic system services. The operating system 216 (e.g., iOS, ANDROID, DARWIN, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks ( e.g ., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components.

[0079] An electronic address 218 is associated with the analysis system 200, which is utilized to at least uniquely identify the analysis system 200 from other devices and components (e.g., client device 300 of Figure 1) of the computer system 100. By way of example, in some embodiments, the electronic address 218 associated with the analysis system 200 is used to provide a source of an analysis provided by analysis system 200 and/or indicate a destination for receiving one or more data elements.

[0080] In some embodiments, the formatting module 220 of the analysis system 200 facilitates modifying a format 222 of a data element that is received and/or retained by the analysis system 200. For instance, in some embodiments, the analysis system 200 receives a first data element in a first format 222-1 from a remote device (e.g. _a from client device 300 of Figure 3), and normalizes the first data element from the first format 222-1 to a second format 222-2 via the formatting module 220, and, in some embodiments, retains the first data element in a third format 222-3. However, the present disclosure is not limited thereto. For instance, in some embodiments, the first data element is retained by the analysis system 200 in the second format 222-2. In this way, the analysis system 200 is capable of receiving a plurality of data elements in one or more predetermined and/or unspecified formats 222, and normalizing the plurality of data elements through the formatting module 220. From this, underlying health information can be derived from the data elements and retained in a normalized format that not only increases the quantity but also quality of health information available for analysis.

[0081] Specifically, each respective format 222 of the formatting module 220 includes a corresponding plurality of characteristics 224 that is specific to the respective format 222. Moreover, each respective characteristic 224 describes a unique aspect of a respective health record, such as treatment described in the respective health record, a patient identifier associated with the respective health record, a dosage of the treatment, and the like. From this, the respective format 222 describes how one or more data elements having health information associated with the corresponding plurality of characteristics 224 of the respective format 222 is retained by the analysis system 200. Not only does the respective format 222 describe retention of the data elements, but also allows for a user of the analysis system 200 to conduct a more detailed analysis on the health information described by the data elements. By way of example, in some embodiments, the plurality of characteristics 224 associated with one or more formats 222 includes a qualitative characteristic ( e.g ., a behavior characteristic, a treatment characteristic, etc.), a quantitative characteristic (e.g., a unit of measurement, etc.), an identifying characteristic (e.g., personally identifiable information (“RP”)), or a combination thereof. However, the present disclosure is not limited thereto. By way of example, in some embodiments, a first format 222-2 is associated with anonymizing the health records of the data elements by removing PII to normalize the health information within the health records. As such, the first format 222-2 includes a first characteristic 224-1 associated a name of a patient is included in a data element, a second characteristic 224-2 associated with an address of the patient (e.g., a mailing and/or billing address of the patient) a third characteristic 224-3 associated with a medical identification of the patient (e.g., an EMR number, a docket number, etc.), or a combination thereof. However, the present disclosure is not limited thereto. This removal and/or normalization of PPI from the data elements not only allows for a user of the analysis system 200 to conduct an analysis of the retained health information but tailor this analysis based on demographic information device from the data elements while omitting obscure information. [0082] In some embodiments, a respective format 222 is a predetermined format 222, such that the predetermined format 222 provides a uniform template for formatting the data elements into in accordance with a determination the data elements are associated with the predetermined format 222. As a non-limiting example, in some embodiments, the predetermined format is an observational health data sciences and informatics (OHDSI) format, which provides a uniform data structure to transform the plurality of elements from the first format to the OHDSI predetermined format. Additional details and information regarding observational health data sciences and information formats can be found at Hripcsak el al, 2015, “Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers,” Studies in Health Technology and Informatics, 216, ph. 574, which is hereby incorporated by reference in its entirety. In some embodiments, the predetermined format 222 is defined by a user (e.g., communicated by a user of a client device 300, provided through the input 210 of the analysis system, etc.), is determined by the analysis system 200 (e.g., through a machine learning process), or a combination thereof. For instance, in some embodiments, the plurality of data elements include first health record of a first patient that includes a first data element of a first weight of the first patient in a first format 222-1 of metric units (e.g., kilograms), a second data element of a second weight of the first patient in the first format 222-1 of metric units, and a third data element of a third weight of a second patient in a second format 222-2 of imperial units (e.g., stones). Accordingly, in this example, a third format 222-2 reflects a normalization of a respective weight of a patient based on a corresponding age of the patient (e.g., a ratio of weight (in pounds (lbs)) to age (in months)), and thus includes a first characteristic 224-1 that facilitates formatting the respective weight of the patient by converting from stones / kilograms to pounds and a second characteristic 224-2 that facilitates formatting the corresponding age of the patient.

[0083] In some embodiments, the characteristic 224 is an age of a respective patient, a gender of the respective patient, an ethnicity of the respective patient, a weight of the respective patient ( e.g ., a body mass of the respective patient, a bone mass of the respective patient, a muscle mass of the respective patient, a fat mass of the respective patient, etc.), a height of the respective patient, a diagnostic information associated with the respective patient, a pharmaceutical composition information associated with the respective patient, a treatment information associated with the respective patient, a site identification associated with the respective patient, a baseline parameter associated with the respective patient, a physical behavioral information associated with the respective patient, an emotional behavioral information associated with the respective patient, a mental information associated with the respective patient, a visit type associated with the respective patient (e.g., an outpatient date, a department type associated with ), a severity of illness scale (e.g., a subjective scale, an objective scale, a combination thereof), a severity of functioning scale, a social history, a stressful and/or benevolent life event, information about one or more side effects from a pharmaceutical composition, information about substance abuse, information about a history of illnesses of close family members or a combination thereof.

[0084] In some embodiments, the analysis system 200 further includes an analysis module 226 that facilitates conducting an analysis on the data elements that have been retained by the analysis system 200. Thus, the analysis module 226 allows a user of the analysis system 200 to realize the computation of a hypothesis created by the user. Specifically, the analysis module 226 includes a tool module 228 that provides a plurality of tools that assist a user in requesting and/or conducting an analysis. By way of example, referring briefly to Figure 6, a first graphical element 640 provides a listing of one or more command line analysis projects associated with a user

[0085] In detail, in some embodiments, the tool module 228 includes a computation tool module 230 that provides one or more computational tools 232 (e.g., first computation tool 232-1, second computation tool 232-2, . . . , computational tool M 223 -M, etc.). Each computation tool 232 utilizes a unique data processing model in a plurality of data processes modules for computing an analysis on a plurality of data elements. For instance, in some embodiments, the unique data processing model associated with a respective computation tool 232 in the plurality of data processing models includes a plurality of statistical analysis models ( e.g ., a comparison of means statistical analysis model associated with first computational tool 232-1), a plurality of time-based models (e.g., a linear fixed effects model associated with a second computational tool 232-2), a plurality of machine learning models (e.g., a random forest model associated with a third computation 232-3), a plurality of user-defined models (e.g., a user defined computational model associated with a fourth computational tool 232-4), or a combination thereof (e.g., a user defined modification to a linear fixed effects model that is associated with fifth computational tool 232-5).

[0086] In some embodiments, the tool module 226 includes a data source tool module 234 that provides one or more data source tools 236. Each data source tool 236 of the data source tool module 234 is associated with a corresponding characteristic 224 in the plurality of characteristics 224. In this way, the one or more data source tools 236 provide a mechanism for a user of the analysis system 200 to manipulate and define the data elements retained by the analysis system 200 that are used in an analysis requested by the user. By way of example, referring briefly to Figure 13,

[0087] Furthermore, in some embodiments, one or more of the above identified data stores and/or modules of the analysis system 200 is stored in one or more of the previously described memory devices (e.g., memory 212), and correspond to a set of instructions for performing a function described above. The above-identified data, modules, or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules. Thus, various subsets of these modules may be combined or otherwise re-arranged in various implementations. In some implementations, the memory 212 optionally stores a subset of the modules and data structures identified above. Furthermore, in some embodiments the memory 212 stores additional modules and data structures not described above.

[0088] Referring to Figure 3, a description of an exemplary client device 300 (e.g., first client device 300-1) that can be used with the present disclosure is provided. In some embodiments, a client device 300-1 includes a smart phone (e.g., an iPhone, an Android device, etc.), a laptop computer, a tablet computer, a desktop computer, a wearable device (e.g., a smart watch, a smart garment, a heads-up display (HUD) device, etc.), a television (e.g., a smart television), or another form of electronic device such as a gaming console, a stand-alone device, and the like. However, the present disclosure is not limited thereto.

[0089] The client device 300 illustrated in Figure 3 has one or more processing units (CPU’s) 302, a network or other communications interface 304, a memory 312 (e.g., random access memory), a user interface 306, the user interface 306 including a display 308 and input 310 (e.g., keyboard, keypad, touch screen, etc.), an optional input/output (I/O) subsystem 330, a power supply 340, one or more communication busses 314 for interconnecting the aforementioned components, or a combination thereof.

[0090] In some embodiments, the user interface 306, the display 308, the input 310, or a combination is as described with respect to the corresponding user interface 306, the corresponding display, the corresponding input 310, or the combination thereof of the analysis system. For instance, in some embodiments, the input 310 is a touch-sensitive display 308, such as a touch-sensitive surface. In some embodiments, the user interface 306 includes one or more soft keyboard embodiments. In some embodiments, the soft keyboard embodiments include standard (QWERTY) and or non-standard configurations of symbols on the displayed icons.

The input 310 and/or the user interface 306 is utilized by an end-user of the respective client device 300 ( e.g ., a respective subject) to input various commands ( e.g ., a push command) to the respective client device 300.

[0091] It should be appreciated that the client device 300 illustrated in Figure 3 is only one example of a multifunction device that may be used with the present disclosure. Thus, a client device 300 optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. The various components shown in Figure 3 are implemented in hardware, software, firmware, or a combination thereof, including one or more signal processing and/or application specific integrated circuits.

[0092] Memory 312 of the client device 300 illustrated in Figure 3 optionally includes high speed random access memory and optionally also includes non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices.

[0093] In some embodiments, the network interface 304 converts electrical signals to from electromagnetic signals and communicates with network 114 and other communications devices, client devices 300 (e.g., a second client device 300-2, client device R 300 -R, etc.), and/or the analysis system 200 via the electromagnetic signals. The network interface 304 optionally includes well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. The network interface 304 optionally communicates with the network 114. In some embodiments, the network interface 304 does not include RF circuitry and, in fact, is connected to the communication network 114 through one or more hard wires ( e.g ., an optical cable, a coaxial cable, or the like).

[0094] In some embodiments, the memory 312 of the client device 300 stores:

• an operating system 316 that includes procedures for handling various basic system services;

• an electronic address 318 associated with the client device 300-1; and

• a client application 320 for communicating a request for an analysis of a candidate subject and/or receiving an analysis of the candidate subject.

[0095] As illustrated in Figure 3, a client device 300 preferably includes an operating system 316 that includes procedures for handling various basic system services. The operating system 316 (e.g., iOS, ANDROID, DARWIN, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components.

[0096] An electronic address 318 is associated with each client device 300, which is utilized to at least uniquely identify a client device 300 from other devices and components of the system 100. In some embodiments, the client device 300 includes a serial number, and optionally, a model number or manufacturer information that further identifies the client device 300. In some embodiments, the electronic address 318 associated with the client device 300 is used to provide a source of a request communicated from the client device 300 (e.g., a request for an analysis of a candidate subject), or to receive an analysis of the candidate subject from an analysis system 200

[0097] A client application 320 is a group of instructions that, when executed by a processor (e.g., CPU(s) 202), generates content (e.g., a visualization of an analysis of a candidate subject provided by the analysis system 200 on the display 308 of the client device; user interface 600 of Figure 6; user interface 700 of Figure 7; user interface 800 of Figure 8, user interface 900 of Figure 9; user interface 1000 of Figure 10; user interface 1100 of Figure 11; user interface 1200 of Figure 12; user interface 1300 of Figure 13; etc.) for presentation to a user of the client device 300. In some embodiments, the client application 320 generates content in response to one or more inputs received from the user through the user interface 306 of the client device 300. For instance, in some embodiments, the client application 320 includes a data presentation application for viewing the contents of a file or web application that includes the analysis of the candidate subject (e.g., in the form of graph, charts, and/or tables). By way of example, the client device 300 communicates a request for an analysis of a candidate subject to the analysis system 200. In some embodiments, the requests includes a request for a visualization of the analysis of the candidate subject, such as a request to output the analysis is on or more graphical representations ( e.g ., tables, figures , charts; graphical user interface 1000 of Figure 10; graphical user interface 1100 of Figure 11; etc.).

[0098] In some embodiments, the client device 300 has any or all of the circuitry, hardware components, and software components found in the system depicted in Figure 3. In the interest of brevity and clarity, only a few of the possible components of the client device 300 are shown to better emphasize the additional software modules that are installed on the client device 300. [0099] Now that details of a computing system 100 for retaining and/or analyzing health information from data elements have been described, details regarding a flow chart of processes and features for implementing a method for retaining health information (e.g., method 400 of Figures 4A and 4B), in accordance with an embodiment of the present disclosure, are disclosed with reference to Figures 4 A and 4B.

[00100] Block 402. Referring to block 402 of Figure 4A, a computer system (e.g., computer system 100 of Figure 1, analysis system 200 of Figure 2, client device 300-1 of Figure 3, etc.) is provided for the retention of information. Specifically, the computer system facilitates the retention of normalized health information obtained from one or more health record. The system 200 includes at least one processor (e.g., CPU 202 of Figure 2) and a memory (e.g., memory 212 of Figure 2). The memory 212 stores at least one program (e.g., formatting module 220 of Figure 2, analysis module 226 of Figure 2, client application 320 of Figure 3, etc.) for execution by the at least one processor 202. Additionally, the at least the program includes one or more instructions for executing a method 400.

[00101] Block 404. Referring to block 404, the method 400 includes receiving, in electronic form, a plurality of data elements from a remote device (e.g., first client device 300-1 of Figure 3, an auxiliary server, etc.). The plurality of data elements includes a plurality of health records associated with a plurality of patients. For instance, in some embodiments, the plurality of data elements includes a plurality of EMRs associated with a corresponding plurality of patients, such that a subset of data elements in the plurality of data elements represents all or some of a first EMR associated with a corresponding first patient in the plurality of patients. In this way, the analysis system 200 receives some or all of the health information included in the health records of the plurality of patients through the data elements. For instance, in some embodiments, the plurality of data elements include the entire electronic health record of a first patient. In other embodiments, a portion of the health record of the first patient is included in the plurality of data elements, such as a cardiovascular portion of the health record of the first patient. However, the present disclosure is not limited thereto. For instance, in some embodiments, the plurality of data elements are received, or derived from a health monitoring device associated with a subject, such as one or more health and passive devices configured to obtain a plurality of data elements ( e.g ., based on one or more measurements obtained from a sensor of a client device 300) including a mobile phone, a wearable device such as a smartwatch or smart garment, a health tracking device, a sleep monitor device, or the like. One of skill in the art will appreciate that the present disclosure is not so limited.

[00102] In some embodiments, the plurality of patients associated with the plurality of health records include at least 10 patients, at least 20 patients, at least 30 patients, at least 40 patients, at least 50 patients, at least 60 patients, at least 70 patients, at least 80 patients, at least 90 patients, at least 100 patients, at least 200 patients, at least 300 patients, at least 400 patients, at least 500 patients, at least 1,000 patients, or at least 5,000 patients. In some embodiments, the plurality of patients associated with the plurality of health records includes a range of patients from about 1 patient to about 10 patients, from about 5 patients to about 25 patients, from about 20 patients to about 45 patients, from about 25 patients to about 60 patients, from about 30 patients to about 90 patients, from about 50 patients to about 100 patients, from about 50 patients to about 200 patients, from about 100 patients to about 200 patients, from about 100 patients to about 500 patients, from about 250 patients to about 1,000 patients, from about 250 patients to about 2,500 patients, from about 500 patients to about 5,000 patients, from about 1,000 patients to about 10,000 patients, or a combination thereof. However, the present disclosure is not limited thereto. [00103] By way of example, in some embodiments, the data elements received by the analysis system 200 includes health information that is publicly available information and/or private information (e.g., confidential and/or privileged). For instance, in some embodiments, the analysis system 200 receives (e.g., polls for) published, publicly available information, such as research papers associated with one or more topics (e.g., scholarly cardiology publications). In some embodiments, the analysis system 200 receives one or more scholarly publications from a client device 300, and some or all of the information included in the one or more scholarly publications is retainable by the analysis system 200, such as one or more novel equations for determining an efficacy of a treatment included in the one or more scholarly publications. From this, the analysis system is capable of learning, either directly through a publication or by conducting an analysis of the publication, state-of-the-art analysis techniques (e.g., computational tools 232 of Figure 2) based on this published information. Accordingly, in some embodiments, the information retained by the analysis system 200 includes information related to a specific field or industry, such as the overall medical industry or a specific field within the medical industry, such as a respective class of pharmaceutical compositions or a respective study of medicine. However, the present disclosure is not limited thereto. For instance, in some embodiments, the specific field or industry is a financial industry ( e.g ., commodity exchanges, cryptocurrencies, etc.), a technological or scientific industry (e.g., semiconductors, solid state fuels, etc.).

[00104] Each respective health record in the plurality of health records includes a characteristic in a plurality of characteristics (e.g., characteristic 224 of Figure 2) of a corresponding patient in the plurality of patients. As described supra , each characteristic 224 describes a unique aspect or parameter of a respective health record, such as a first characteristic 224-1 in the plurality of characteristics 224 is associated with a name of a patient, a second characteristic 224-2 in the plurality of characteristics 224 is associated with a blood cell count of a patient, a third characteristic 224-3 in the plurality of characteristics 224 is associated with a dosage of a pharmaceutical composition treatment for a medical condition of the patient, and the like. In this way, the analysis system 200 receives the data elements that includes the underlying data of the health records of the plurality of patients in order to consolidate the health information of the plurality of patients for future analysis or use (e.g., method 500 of Figure 5 A through Figure 5C). [00105] Block 406. Referring to block 406, in some embodiments, the plurality of characteristics 224 includes a plurality of qualitative characteristics 224 (e.g., a first characteristic behavior characteristic 224-1, a second treatment characteristic 224-2, etc.), a plurality of quantitative characteristics 224 (e.g., a third unit of measurement characteristic 224- 3, etc.), a plurality of identifying characteristics 224 (e.g., a PII characteristic 224), or a combination thereof. However, the present disclosure is not limited thereto. For instance, as described infra, in some embodiments, the plurality of characteristics 224 include a plurality of temporal characteristics 224, such as an entry date of a respective characteristic 224 of a health record or a date of diagnosis of the respective characteristic 224. From this, the various classifications of the characteristics 224 allows for granular, collective retention of the health information within the plurality of data elements.

[00106] Each respective qualitative characteristic 224 describes a unique subjective and/or non- quantitative aspect of a health record of a patient, such as a behavioral characteristic 224 of a respective patient or a treatment characteristic 224 of the respective patient. For instance, in some embodiments, removing the subjective, qualitative characteristics 224 from a data set (e.g., retrieved data elements in response to a request for an analysis of a candidate subject, block 504 of Figure 5 A, etc.) in order to focus the analysis on purely quantitative characteristics 224 of the health records. In some embodiments, a qualitative behavioral characteristic 224 of a respective patient includes a first characteristic 224-1 associated with a qualitative impulsivity of the respective patient ( e.g ., as indicated by the respective patient and/or a medical practitioner associated with the respective), a second characteristic 224-2 associated with a qualitative attention deficit hyperactivity disorder (ADHD), etc. However, the present disclosure is not limited thereto.

[00107] On the other hand, each respective quantitative characteristic 224 describes a unique objective and/or quantifiable aspect or parameter of a health record of a patient, such as a respective measurement for a type of health information. By way of example, in some embodiments, the plurality of characteristics 224 includes a first quantitative characteristic 224 that is associated with a blood pressure of a respective patient, such that the data elements associated with the blood pressure of a respective patient are formatted in accordance with the first quantitative characteristic 224, such as in units of millimeters of mercury (mmHg).

[00108] In some embodiments, the plurality of characteristics 224 includes an identifying characteristic (e.g., personally identifiable information (“RP”)), which is utilized to particularly identify a corresponding patient. As a non-limiting example, in some embodiments, a plurality of data elements received by the analysis system 200 includes a first health record of a corresponding patient that includes: a name of the corresponding patient in a first format 222 (e.g., last name, middle initial, first name); an age of the corresponding patient in a second format (e.g., numeric value indicating years), a gender of the corresponding patient (represented by a letter), an address associated with the corresponding patient, a weight associated with the corresponding patient, diagnostic information associated with the corresponding patient (e.g., patient has a positive diagnosis for Type-II diabetes, patient has negative diagnosis for cardiovascular disease, etc.), a treatment of the corresponding patient (e.g., one or more pharmaceutical compositions previously and/or currently consumed by the patient), or a combination thereof. In this example, the health record includes one or more PII characteristics 224 (e.g., name of patient, address of patient), one or more quantitative characteristics 224 (e.g., age, weight), one or more qualitative characteristics (e.g., sex, diagnostic information, treatment information). Accordingly, the identifying characteristics 224 allow for the removal of PII when retaining the plurality of data elements while maintaining the prior relations of the health information that was otherwise linked through the PII, such as trends over time in a blood pressure measurement characteristic 224 over a period of time.

[00109] Block 408. Referring to block 408, in some embodiments, the plurality of qualitative characteristics 224 includes a first qualitative characteristic 224-1 associated with a diagnosis of a medical condition. In some embodiments, the diagnosis of the medical condition includes a neuropsychiatric disorder, a physical disorder, a behavior disorder, a disease, or a combination thereof. However, the present disclosure is not limited thereto.

[00110] In some embodiments, the diagnosis of the neuropsychiatric disorder medical condition includes an epileptic medical condition, a cognitive deficit medical condition, dementia, and the like.

[00111] In some embodiments, the diagnosis of the physical disorder medical condition includes a disease based medical condition ( e.g ., coronary heart disease, crones disease, etc.), a bodily medical condition (e.g., amputation, etc.), and the like.

[00112] In some embodiments, the diagnosis of the behavioral disorder medical condition associated with a respective qualitative characteristic 224 includes a sleep behavior of the respective patient (e.g., the respective patient is a self-described “early bird” and is an initial person awake in their household), an exercise behavior of the respective patient (e.g., the respective patient is self-described as frequently exercising), an activity level of the respective patient (e.g., the respective patient is self-described as having a sedentary lifestyle), or a combination thereof. As a non-limiting example, in some embodiments, the diagnosis of a behavior disorder associated with a respective qualitative characteristic 224 of the data elements includes one or more physician notes included in a health record of a corresponding patient. In some embodiments, the one or more physician notes is directed to a questionnaire, or survey, associated with the corresponding patient. For example, in some embodiments, the corresponding patient and/or the medical practitioner associated with the corresponding patient completes a survey relating to one or more moods of the patient (e.g., qualitative characteristics 224). The physician in this example takes notes based on the responses provided by the corresponding patient based on their mood. These notes can then be considered when conducting an analysis based on the health information of the corresponding patient (e.g., method 500 of Figures 5 A through 5C) or tracking health information of the corresponding patient over a period of time. For example, the same survey can be administered to the corresponding patient at different time intervals, and the responses compared by the analysis system 200 at each of the different time intervals. In some embodiments, these physician notes are directed to information discussed during an appointment. For example, a psychiatric patient may describe a situation in which the patient experienced specific symptoms to the physician during their appointment. In this example, the physician records the symptoms of the patient in the corresponding health record. [00113] In some embodiments, the first qualitative characteristic 224-1 that includes the diagnosis of the medical condition is a positive diagnosis ( e.g ., a respective patient is known to have a respective medical condition, such as an adverse reaction to a first class of pharmaceutical compositions), a negative diagnosis (e.g., a respective patient is known to have an immunity to the respective medical condition, had and no longer has the respective medical condition, etc.), an indetermination diagnosis (e.g., unable to draw definitive conclusion as to diagnosis of the medical condition), or the like. However, the present disclosure is not limited thereto.

[00114] In some embodiments, the first qualitative characteristic 224-1 associated with a diagnosis of a medical condition includes one or more ICD-10-CM codes and/or one or more ICD-10-PCS codes. However, the present disclosure is not limited thereto.

[00115] In some embodiments, the diagnosis of the medical condition associated with a respective qualitative condition 224 is a consanguineous diagnosis of the medical condition. For instance, in some embodiments, the patient has a consanguineous relation with a corresponding patient that, in turn, has a positive diagnosis of the medical condition. In this way, the analysis 200 can retain the health information of the positive consanguineous diagnosis of the medical condition, allowing for an analysis of one or more trends based on diagnoses of medical condition through consanguineous relations (e.g., block 538 of Figure 5C).

[00116] Block 410. Referring to block 410, in some embodiments, the plurality of qualitative characteristics 224 includes a respective qualitative characteristic 224 (e.g., a second qualitative characteristic 224-23) that is associated with a treatment of a medical condition.

[00117] In some embodiments, the treatment of a medical condition associated with the respective qualitative characteristic 224 includes a therapy, such as a physical therapy treatment. In some embodiments, the treatment of a medical condition associated with the respective qualitative characteristic 224 includes a pharmaceutical composition, or, similarly, a class of pharmaceutical compositions.

[00118] In some embodiments, the treatment is past treatment information based on an earlier diagnosis, such as a successful past treatment for a first medical condition diagnosis or a failed past treatment for the first medical condition. In some embodiment, the treatment is a current treatment related to a recent or continuing diagnosis, such as an ongoing radiation treatment therapy. For example, in some embodiments, the treatment information includes an expired prescription for a first pharmaceutical composition that treats post-traumatic stress disorder and a current, active prescription for a second pharmaceutical composition that treats depression. In this example, consumption of the first pharmaceutical composition is associated with a first characteristic 224-1 and consumption of the second pharmaceutical composition is associated with a second characteristic 224-2.

[00119] By classifying various treatments of medical conditions through the plurality of qualitative characteristics, a user of the present disclosure is afforded opportunities to requests and conduct analyses ( e.g ., method 500 of Figures 5 A through 5C) based on hypothesized trends in one or more treatments identified in the user.

[00120] Block 412. Referring to block 412, in some embodiments, the plurality of qualitative characteristics 224 includes a respective qualitative characteristic 224 (e.g., a third qualitative characteristic 224-2) that is associated with a demographic of a respective patient in the plurality of patients (e.g., fifth characteristic 224-5 of Figure 7). In some embodiments, the demographic of the respective patient includes a geographic demographic associated with the respective patient, such as an address of the respective patient (e.g., a country, a region, a state, a city, a zip code, or combination thereof associated with the respective patient), an address associated with a medical practitioner associated with the respective patient, or both. However, the present disclosure is not limited thereto. By utilizing geographic demographic qualitative characteristics 224, the system 200 can categorize and/or form relations between health information of the health records of the data elements based on one or more geographic demographic distinctions, such as a distinction between a first plurality of patients associated with a first geographic demographic qualitative characteristics 224-1 further associated with the northern hemisphere as compared to a second plurality of patients associated with a second geographic demographic qualitative characteristics 224-2 further associated with the southern hemisphere. However, the present disclosure is not limited thereto.

[00121] For instance, in some embodiments, the demographic qualitative characteristics 224 of the respective patient includes a race of the respective patient (e.g., an ethnicity of the respective patient, a heritage of the respective patient), a gender of the respective patient, an employment status of the respective patient (e.g., an income value of the respective patient, a type of labor of the respective patient, etc.), an education status of the respective patient (e.g., a degree status of the respective patient, an education test score of the respective patient), a marital status of the respective patient (e.g., single, never married, divorced, widowed, etc.), and the like. However, the present the present disclosure is not limited thereto.

[00122] Block 414. Referring to block 414, in some embodiments, the plurality of quantitative characteristics 224 includes a plurality of quantitative temporal characteristics 224, a plurality of quantitative measurement characteristics 224, or both. Each quantitative temporal characteristic 224 describes a temporal aspect related to an aspect of a health record including a date of a medical visit associated with a respective patient, an initial date associated with a treatment of a medical condition of the respective patient, a final date associated with the treatment of the medical condition of the respective patient, a date of entry into the heath record, a date of diagnosis of the medical condition, and the like. In some embodiments, the temporal characteristic 224 is included within the plurality of data elements, such as a detailed entry in an EMR including a date of entry of a diagnosis for a bone disease medical condition, a date of laboratory testing for the bone disease medical condition ( e.g ., date of sample collection, date of results, type of testing conducted on the sample, etc.), and a date of visit for symptoms of the bone disease medical condition. In other embodiments, the temporal characteristic 224 is inferred from the plurality of data elements, which allows for generating temporal data elements that otherwise would not exists for analytical purposes. By way of example, consider a less detailed entry in comparison to the above detailed entry, in that the less detailed entry includes a first dated health record including the diagnosis for the bone disease medical condition and a second dated health record including the date of laboratory testing for the bone disease medical condition. From this, a first temporal characteristic 224-1 is associated with a second medical condition characteristic 224-2 from the first dated health record (e.g., the diagnosis) and a third temporal characteristic 224-3 is associated with a fourth medical condition characteristic 224-4 from first dated health record (e.g., the laboratory test), which allows for normalization of the health information from the health records of the patient.

[00123] In some embodiments, the plurality of quantitative measurement characteristics 224 includes one or more measurements associated with or obtained from a respective patient. For instance, in some embodiments, the plurality of quantitative measurement characteristics 224 includes an age of a respective patient, a severity of a medical condition of the respective patient expressed by a numerical value (e.g., a linear scale of severity of a medical condition, a logarithmic scale of severity of a medical condition, etc.), a biological measurement associated with or obtained from the respective patient (e.g., a blood pressure measurement obtained from the respective patient, a height of the respective patient, a weight of the respective patient, a lung capacity of the respective patient, etc.), or a combination thereof.

[00124] For instance, consider a first patient that is concerned with his or her weight. The patient visits a medical practitioner for weight measurements twice a month over a period of six months totally twelve weight measurements. In this example, a first quantitative measurement characteristic 224-1 is associated with the weight of the patient and a second quantitative temporal characteristics 224-2 is associated with a date of entry of measurement. From this, an association is formed between the different weight measurements through the first quantitative measurement characteristic 224-1 and the second quantitative temporal characteristics 224-2, which allows for conducting an analysis on a candidate subject based on one or more temporal trends.

[00125] Block 416. Referring to block 416, the method 400 includes normalizing the plurality of data elements. By normalizing the plurality of data elements, the analysis system 200 forms a plurality of normalized data elements that is based on, and includes the same or more health information as, the plurality of data elements. Specifically, the normalizing of the plurality of data elements includes, for each respective data element in the plurality of data elements, formatting the respective data element from a first format (e.g., first format 222-1 of Figure 2) to a predetermined format (e.g., third format 222-3 of Figure 2) (e.g., formatting via formatting module 220 of Figure 2). By formatting the respective data element from the first format 222-1 to the predetermined format 222, the health information associated with a respective characteristic 224 is retained in a uniform format for each respective patient in the plurality of patients.

[00126] In some embodiments, the first source is associated with a source of a corresponding health record, such as an author source (e.g., a specific medical practitioner) and/or a file type (e.g., a specific EMR configuration) of the corresponding health record. For instance, in a simple example, a first clinic stores weight data elements for a first plurality of patients in kilograms and a second clinic stores weight data elements for a second plurality of patients in pounds, such that the data elements received from both the first clinic and the second clinic are standardized for a predetermined format 222 for a weight of a respective (e.g., kilograms). [00127] In some embodiments, the formatting of the respective data element from the first format 222-1 to the predetermined format 222 includes forming a temporal data element associated with the respective data element. By way of example, consider a corresponding health record of a first patient that includes a plurality of data elements associated with a core temperature measurement of the first patient in a first format 222-1 of Kelvin, a treatment of a steroid injection provided to the first patient in a second format 222-2 of fluid ounces, and a date of visit by the first patient in a third format 222-3 of Month, Day, Year. Accordingly, the analysis system 200 receives the plurality of data elements and forms a plurality of normalized data elements that includes the core temperature measurement of the first patient in a fourth format 222-4 that includes a first measurement characteristic 224-1 associated with a core temperature of a respective patient and a second temporal characteristic 224-2 associated with a date of capture in days, and a fifth format 222-4 that includes a third measurement characteristic 224-3 associated with a volume of an injection in milliliters (mL) and the second temporal characteristic 224-2. From this, the health information associated with the core temperature measurement and the application of the treatment are retained by the analysis system 200 in a normalized format. Moreover, this health information is further, individually associated with the temporal characteristics, such that individual temporal trends can be evaluated through the present disclosure.

[00128] In some embodiments, the normalizing includes correcting spelling mistakes found in a respective health record, such as an obvious clerical error such as a blood type inadvertently machine translated from the letter “B” to the number “13” when converting the respective health record into an electronic format. In some embodiments, the normalizing includes removing free text of the respective health record, such as removing ad hoc characters added by a particular medical practitioner that have meaning only to that particular medical practitioner. Additionally, in some embodiments, the normalizing of the plurality of data elements includes converting one or more entries in in the respective health record for clarity and/or concision, such as reflecting the same type of medical code ( e.g ., a first “DSM IV” and a second “DSM 5” entry that refer to the same fifth DSM code).

[00129] Block 418. Referring to block 418 of Figure 4B, in some embodiments, the method 400 includes determining if a respective health record in the plurality of health records is associated with a respective measurement in a plurality of measurements. For instance, in some embodiments, the analysis system 200 determines if a respective data element associated with the respective health record is associated with a respective quantitative characteristic 224. In this way, if the respective data element is associated with the respective quantitative characteristic 224, then a predetermined format 222 is available normalizing the respective data element.

Thus, in accordance with a determination that the respective health record is associated with the respective measurement, the method 400 includes formatting the respective health record from the first format 222-1 to the predetermined format 222. However, the present disclosure is not limited thereto.

[00130] In some embodiments, the determining if the respective health information is associated with the respective measurement includes utilizing one or more instructions (e.g., one or more formats 222 of Figure 2) that describe a predetermined association between a predetermined health information and a corresponding predetermined measurement.

[00131] Block 420. Referring to block 420 of Figure 4B, in some embodiments, the first format is an electronic medical record format. In some embodiments, the electronic medical record format is received from a public database. In some embodiments, the electronic medical record format is received from a clinical trial. In some embodiments, the electronic medical record format is received from a hospital or clinic. In some embodiments, the medical record includes information relating to the patient ( e.g ., pharmaceutical composition information, a personal identifier, a treatment information, a pre-existing condition, etc.).

[00132] Block 422. Referring to block 422 of Figure 4B, in some embodiments, the predetermined format 222 the system 200 normalizes a respective data element into is a quantitative format 222, in that the predetermined format 222 is associated with a quantitative characteristic 224, such as a weight of a respective patient, an age of the respective patient, and the like. However, the present disclosure is not limited thereto.

[00133] In some embodiments, one or more computational tools 232, such as a T test model computational tool 232, a c ² test model computational tool 232, a logistic regression model computational tool 232, a linear regression model computational tool 232, conduct an analysis on a normalized data elements having two characteristics 224 (i.e., two dimensions), which are temporally independent. On the other hand, in some embodiments, one or more computational tools 232 such as a time-to-event model computational tool 232, a survival model computation tool 232, and the like, specifically conduct analysis of the normalized data elements along a time, and hence must incorporate a temporal characteristic 224. However, the present disclosure is not limited thereto. For instance, these time-dependent computational tools 232 are configured to receive one or more two-dimensional tables as inputs, where one of the axes of a respective two- dimensional table is time.

[00134] In some embodiments, the patient identifier associated with the respective patient is distinct from the PII characteristics 224 of the respective health record, in that the analysis system 200 generates the patient identifier during the normalizing of the plurality of data elements. For instance, in some embodiments, the patient identifier is uniquely generated based on a random number generator (e.g., a quantum generator), a concatenation one or more characteristics 224 of a respective health record (e.g., an age of a respective patient, a gender of a respective patient, etc.), or a combination thereof.

[00135] In some embodiments, conducting an analysis requires utilizing health information obtained from health records over a period of time, given the longitudinal nature of a health record. Because of this temporal nature of health records, the predetermined format 222 of the present disclosure allows health information to be retained and analyzed with greater ease. By way of example, consider health information of a first patient that is normalized as a predetermined format 222, and includes a first characteristic 224-1 dimension associated with a patient identifier (pi), a time point (il), and an identifier that identifies a type of health information characteristic 224 of this data point (VI ). As described supra, in some embodiments, the type of health information characteristic 224 of this data element at this point can be any arbitrary character tic 224, such as an age of a respective patient. In some embodiments, the type of health information characteristic 224 is arbitrarily complex. As a non-limiting example, on a particular day, the first patient might be administered five different pharmaceutical composition treatments with different doses and regimens. Furthermore, each of the different pharmaceutical composition treatments includes at least two different active ingredients in different molar concentrations. Accordingly, this entire set of health information is represented by a first format 222-1 (ul,pl, tl).

[00136] Additionally, in some embodiments, analyzing health information over a period of time is required, such as analyzing a cognitive score as an average score over a week period of time before a diagnosis of a particular medical condition to a time at which the diagnosis was considered (/ I l2). Moreover, in some embodiments, this range in time might be different for different respective patients, and can include patients that do not have the particular medical condition, such as a false positive or preventative measure treatment. In another example, the analysis occurs over a range of time. Accordingly, this entire set of health information is represented by a second format 222-2 (ul, pi, tl tl). Furthermore, in some embodiments, such as utilizing a time independent computational model ( e.g ., T tests computational model 232, etc.), a respective characteristic 224 of interest must be isolate from time variance resulting in a third, two-dimensional hyperspace format 222-3 (ul,pl). Accordingly, the present disclosure allows for the retention of normalized data elements in a plurality of formats 222 that coincide with one or more needs of the tools 228 available to a user, and application of these retained data elements to a time dependent computational model 232, a time independent computational model 232, or a combination thereof.

[00137] Block 432. Referring to block 432 of Figure 4B, in some embodiments, the quantitative format 222 is a unit of measurement from a coherent system of units. By way of example, in some embodiments, the unit of measurement of the quantitative format 222 includes a length (e.g., a millimeter (mm)), a mass (e.g., a kilogram (kg)), a time (e.g., a second (sec)), a current (e.g., an ampere (A)), a temperature (e.g., Kelvin (K)), or a combination thereof (e.g., millimeter mercury (mm Hg)). However, the present disclosure is not limited thereto. In some embodiments, the unit of measurement of the quantitative format 222 is a dimensionless unit of measurement, such as an elasticity of the dermis of a patient or a binary indicator (e.g., 0 for negative diagnosis, 1 for positive diagnosis, etc.), and the like.

[00138] Block 434. Referring to block 434 of Figure 4B, the method 400 includes retaining in the memory 212 of the analysis system 200 the plurality of normalized data elements normalized from the plurality of data elements received by the analysis system 200. Thus, the present disclosure retains the health information of the heath records from the plurality of data elements in a normalized format that not only provides improved data hygiene but also enhances the quality of health information obtained from the health records.

[00139] Now that details of a method 400 for facilitating retention of a plurality of data elements have been disclosed, details regarding a method 500 for facilitating an analysis of a candidate subject, in accordance with an embodiment of the present disclosure, are disclosed with reference to Figure 5A through Figure 5C.

[00140] Block 502. Referring to block 502 of Figure 5 A, the method 500 includes receiving, in electronic form, a request for an analysis of a candidate subject from a remote device. In some embodiments, the remote device is a client device ( e.g ., client device 300 of Figure 1, client device 300 of Figure 3, etc.). However, the present disclosure is not limited thereto.

[00141] The candidate subject is provided to describe a domain of the analysis. For instance, in some embodiments, an analysis system 200 retains (e.g., method 400 of Figure 4A and Figure 4B) a plurality of data elements (e.g., normalized data elements of block 416 of Figure 4A) that includes one or more categorizations, such as a source of a respective data element (e.g., a clinic that provided the respective data element) and read/write privileges to the respective data element. By way of example, a user has access to a first plurality of data elements associated with a first plurality of patients having a further association with a cardiovascular medical condition (e.g., health records provided by a respective cardiovascular research entity) stored in a first portion of a database (e.g., memory 212 of Figure 2, memory 312 of Figure 3, etc.) and a second plurality of data elements associated with a second plurality of patients having a further association with a respective cognitive medical condition (e.g., health records provided by a post-traumatic stress disorder research entity) stored in a second portion of the database. From this, the user can select as the candidate subject the first portion of the database as a first domain of a first analysis, the second portion of the database as a second domain of a second analysis, a third portion of the data base including some or all of the first portion of the database and the second portion of the database, or a combination thereof. However, the present disclosure is not limited thereto.

[00142] For instance, in some embodiments, the candidate subject of the request includes one or more characteristics 224 (e.g., characteristics 224 of Figure 2, characteristics 224 of block 404 to block 414 of Figure 4A, etc.), such as a respective qualitative characteristic 224, a respective quantitative characteristic 224, a respective demographic characteristic 224, and the like. Accordingly, a user can define a domain of a requested based on the one or more characteristics 224 of the candidate subject, which ensures that the analysis considers relevant data element and excludes others that are less pertinent by lacking association with the one or more characteristics 224.

[00143] Block 504. Referring to block 504, the method 500 includes retrieving, in response to the request for the analysis of the candidate subject, a plurality of data elements associated with the candidate subject from a database ( e.g ., memory 212 of Figure 2, memory 312 of Figure 3, etc.). By way the prior example of block 502, in some embodiments, the method 500 retrieves each respective data element from the first portion of the data in respect to the user selecting the first portion of the database as the candidate subject. However, the present disclosure is not limited thereto. For instance, in some embodiments, the plurality of data elements retrieved in response to the request includes a listing (e.g., visualization 810 of Figure 7) of one or more characteristics 224 associated with the candidate subject, which provides an overview of the health information associated with the retrieved data elements. In some embodiments, the database includes a data lake. In some embodiments, the database base includes a data warehouse.

[00144] By way of example, referring briefly to Figure 7, a graphical user interface 700 provides a candidate subject visualization 810. In exemplary embodiment of Figure 7, the candidate subject is a specific database selected by the user, such as a tailored subset of the data elements retained by the analysis system 200 or a specific version of these retained data elements. In some embodiments, the candidate subject visualization 810 provides a corresponding listing of one or more characteristics 224, or classifications of characteristics 224, associated with the retained data elements that are retrieved in response to the candidate subject. By way of example, the candidate subject visualization 810 provides a corresponding listing of a first characteristic 224-1 associated with an age of a respective patient, a second characteristic 224-2 associated with one or more pharmaceutical composition treatments (i.e., “Meds”) taken by the respective patient, a third characteristic 224-3 associated with a race of the respective patient, a fourth characteristic 224-4 associated with a gender (i.e., sex) of the respective patient, a fifth characteristic 224-5 associated with site identifier of the respective patient (e.g., an anatomical site identifier such as a first identifier for the left wrist of the respective patient and a second identifier for the right wrist of the respective patient; a geographic site identifier such as a geographical region identifier), a sixth characteristic 225-6 associated with a baseline temporal date of a parameter of a health record of the respective patient, a seventh characteristic 224-7 associated with a diagnostic and statistical mental disorder of the respective patient, an eighth characteristic 224-8 associated with one or more health facility visit types by the respective patient, and a ninth characteristic 224-9 associated with a clinical global impression severity score of the respective patient. Thus, at least one data element retrieved from selection of the database candidate subject is associated with at least one of the aforementioned characteristics 224. However, the present disclosure is not limited thereto.

[00145] Block 506. Referring to block 506 of Figure 5 A, the method 500 includes receiving, based on the retrieving, a selection of a respective tool ( e.g ., first computational tool 232-1) in a plurality of tools (e.g., tool 226 of Figure 2) from the remote device 300. The plurality of tools 226 includes a plurality of computational tools (e.g., computational tools 232 of Figure 2) and a plurality of data source tools (e.g., data source tools 236 of Figure 2).

[00146] Each respective computational tool 232 in the plurality of computational tools 232 utilizes a unique data processing model in a plurality of data processing models in analyzing a plurality of data elements. As a non-limiting example, consider a first computation tool 232-1 associated with an algebraic data processing model and a second computational tool 232-2 associated with a Boolean data processing model. Accordingly, a user can select and order the first computational tool 232-1 and the second computation tool 232-2 to define hypothesized computations. However, the present disclosure is not limited thereto.

[00147] Each respective data source tool 236 in the plurality of data source tools 236 is associated with a corresponding characteristic in a plurality of characteristics 224 (e.g., characteristics 224 of Figure 2, characteristics 224 of Figure 7, etc.).

[00148] In some embodiments, the receiving a selection of a respective tool in a plurality of tools from the remote device is conducted in response to the retrieving a plurality of data elements associated with the candidate subject from a database (e.g., the plurality of tools available for selection is based on the candidate subject). By way of example, referring briefly to Figure 7, a candidate subject visualization 810 allows a user to select one or more corresponding characteristics 224 that is retrieved based on an association with a candidate subject for analysis. In the graphical user interface 700 of Figure 7, the one or more corresponding characteristics 224 associated with the candidate subject visualization 810 are dragged and dropped into an affordance region 730 that allows construction of a computational request for an analysis through a visual programming interface. However, the present disclosure is not limited thereto. The affordance region 730 includes of at least two nodes (e.g., first node 740-1 of Figure 7) that each represent a respective computational tool 232 or a respective data source tool 236 provided by the analysis system 200. By way of example, in some embodiments, the one or more nodes 740 include a first source node 740-1 associated with a first data source tool 236-1 and a second computational node 740-2 associated with a first computational tool 232-1. Accordingly, a respective source node 740 describes what data elements are retrieved and utilized when conducting an analysis of the candidate subject. In some embodiments, the one or more nodes 740 include a third sink node 730-1, which is a representation of a visualization ( e.g ., associated with a visualization computational tool 232) that is performed on outputted data elements that result from one or more computational nodes 740 associated with a respective statistical model computational tool 232 and/or a respective machine learning model computational tool 232. Additionally, in some embodiments, the affordance region 730 provides one or more edges 750 that interconnect the one or more nodes 740, such as describing how an output of a first node is received by an input of a second node.

By way of example, the affordance region 730 of Figure 7 includes a first edge 750-1 interconnecting a first output of first data source node 740-1 and a first input of a seventh computational node 740-7, and a second edge 750-2 interconnecting a second output of a third data source node 740-3 with a second input of a fourth data source node 740-4 and a third input of the fourth data source node 740-4. However, the present disclosure is not limited thereto. [00149] Additionally, the graphical user interface 700 of Figure 7 is depicted for providing a request for analysis of a candidate subject, in accordance with an embodiment of the present disclosure. Specifically, Figure 7 depicts the graphical user interface 700 that allows a user to graphically define a request for an analysis of a candidate subject, such as one or more computations that will convert a hypothesis of the user into an analysis through the analysis system 200. The graphical user interface 700 is designed to provide a visual programming interface for a user to define the request based on the selection of one or more computational tools 232 and one or more data source tools 236. However, by utilizing the retrieved, retained, normalized data elements, the visual programming interface provided by the graphical user interface 700 allows for concurrent use of at least one time-dependent computational tool 232 and at least one time-dependent computational tool 232. Hence, the time-independent computational tool 323 is now available for use on the health information retained by the analysis system 200 that initially included a time dependent variable (e.g., use on a two- dimensional hyperspace format 222 based on a three-dimensional, time inclusive characteristic 224).

[00150] Block 508 to block 522. Referring to block 508 of Figure 5 A to block 522 of Figure 5B, in some embodiments, the plurality of data processing models utilized by the plurality of computational tools 232 includes a plurality of statistical analysis models (e.g., time independent computational tools 232), a plurality of time-based models (e.g., time dependent computational tools 232), a plurality of machine learning models, a plurality of user-defined models, or a combination thereof.

[00151] In some embodiments, the plurality of statistical analysis models associated with one or more computational tools 232 includes a plurality of correlation models. In some embodiments, the plurality of correlation models includes a continuous variable correlation model, an ordinal correlation model, or both. In some embodiments, the continuous correlation model is a Pearson test, and the ordinal correlation model is a Spearman test.

[00152] In some embodiments, the plurality of statistical analysis models associated with one or more computational tools 232 includes a plurality of comparison models. In some embodiments, the plurality of comparison models includes one or more comparison of means models.

Moreover, in some embodiments, the one or more comparison of means models includes a Z-test model, a paired T-test model, an independent T-test model, a Chi-square test model, an analysis of variance model, or a combination thereof.

[00153] In some embodiments, the plurality of statistical analysis models associated with one or more computational tools 232 includes a plurality of regression models. In some embodiments, the plurality of regression models includes a linear regression model.

[00154] In some embodiments, the plurality of statistical analysis models associated with one or more computational tools 232 includes a plurality of classification models. In some embodiments, the plurality of classifications models includes a logistic regression model, a score classification model, or both. Moreover, in some embodiments, the score classification model includes a contingency table for logistic regression score model, an area under a receiver operating characteristic curve score model, an FI score model, a Brier score loss model, a specificity score model, a sensitivity score model, a prevalence score model, a true positive rate score model, a false positive rate score model, a positive predictive value score model, a negative predictive value score model, or a combination thereof.

[00155] In some embodiments, the plurality of statistical analysis models associated with one or more computational tools 232 includes a plurality of survival analysis models.

[00156] In some embodiments, the plurality of statistical analysis models associated with one or more computational tools 232 includes a plurality of product limit estimation models.

[00157] In some embodiments, the plurality of statistical analysis models associated with one or more computational tools 232 includes a plurality of ranking models.

[00158] In some embodiments, the plurality of statistical analysis models associated with one or more computational tools 232 includes a plurality of cox proportional hazard models. [00159] Block 524. Referring to block 524 of Figure 5B, in some embodiments, the plurality of time-based models associated with one or more computational tools 232 includes one or more linear fixed effect models. In some embodiments, the plurality of time-based models associated with one or more computational tools 232 includes one or more linear random effect models. In some embodiments, the plurality of time-based models associated with one or more computational tools 232 includes one or more time to event models. In some embodiments, the plurality of time-based models associated with one or more computational tools 232 includes one or more exposure-effect correlation models.

[00160] Block 526. Referring to block 526, in some embodiments, the plurality of machine learning models associated with one or more computational tools 232 includes one or more random forest models.

[00161] In some embodiments, the plurality of machine learning models associated with one or more computational tools 232 includes one or more random survival forest models.

[00162] In some embodiments, the plurality of machine learning models associated with one or more computational tools 232 includes one or more extreme gradient boosting models. Extreme gradient boosting models are described in Chen et al. , 2015, “Xgboost: Extreme Gradient Boosting,” R package version 0.4-2, pg. 1; Wang et al, “Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model,” Transportation Research Record, 2672(47), pg. 35, each of which is hereby incorporated by reference in its entirety.

[00163] In some embodiments, the plurality of machine learning models associated with one or more computational tools 232 includes one or more support vector machine (SVM) models.

SVM models are described in Cristianini and Shawe-Taylor, 2000, “An Introduction to Support Vector Machines,” Cambridge University Press, Cambridge; Boser et al., 1992, “A training algorithm for optimal margin classifiers,” in Proceedings of the 5 ^th Annual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, Pa., pp. 142-152; Vapnik, 1998, Statistical Learning Theory , Wiley, New York; Mount, 2001, Bioinformatics: sequence and genome analysis , Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.; Duda,

Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc., pp. 259, 262-265; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York; and Furey et al., 2000, Bioinformatics 16, 906-914, each of which is hereby incorporated by reference in its entirety. When used for classification, SVMs separate a given set of binary labeled data training set with a hyper-plane that is maximally distant from the labeled data. For cases in which no linear separation is possible, SVMs can work in combination with the technique of “kernels,” which automatically realizes a non-linear mapping to a feature space. The hyper-plane found by the SVM in feature space corresponds to a non-linear decision boundary in the input space. In some embodiments, the plurality of weights associated with the SVM define the hyper-plane. In some embodiments, the hyper-plane is defined by at least 10, at least 20, at least 50, or at least 100 weights and the SVM classifier requires a computer to calculate because it cannot be mentally solved.

[00164] In some embodiments, the plurality of machine learning models associated with one or more computational tools 232 includes one or more Gaussian mixture models. Gaussian mixture models are described in Reynolds, 2009, “Gaussian Mixture Models,” Encyclopedia of Biometrics, pg. 741; Zong et al. , 2018, “Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection,” International Conference on Learning Representations, print; Rasmussen, 1999, “The Infinite Gaussian Mixture Model,” Advances in Neural Information Processing Systems, (12), pg. 554, each of which is hereby incorporated by reference in its entirety.

[00165] Additionally, in some embodiments, the plurality of machine learning models associated with one or more computational tools 232 includes one or more neural network models. Neural network models, including convolutional neural network models, are disclosed in Vincent et al. , 2010, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” J Mach Learn Res 11, pp. 3371-3408; Larochelle et al. , 2009, “Exploring strategies for training deep neural networks,” J Mach Learn Res 10, pp. 1-40; Hassoun, 1995, Fundamentals of Artificial Neural Networks, Massachusetts Institute of Technology; and Duda, Pattern Classification , Second Edition, 2001, John Wiley & Sons, Inc., each of which is hereby incorporated by reference.

[00166] Block 528. Referring to block 528, in some embodiments, the plurality of user-defined models associated with one or more computational models 323 includes one or more user- defined logic gate models. In some embodiments, the one or more user-defined logic gate models includes a user-defined Boolean logic. As a non-limiting example, in some embodiments, the user defined logic gate model associated with a first computational tool 232 includes a determination that if a first characteristic 224-1 exclusive or (XOR) a second character 224-1, then provide user define output.

[00167] As another non-limiting example, referring briefly to Figure 13, a graphical user interface 300 for determining a user defined gate model associated with a respective computational tool 232, in accordance with an embodiment of the present disclosure.

Specifically, the graphical user interface 1300 of Figure 13 provides a cohort builder 1310 for define a user-defined logic gate model 1320. The user-defined logic gate model 1320 includes a set of parameters that define a structure of the model, such as inclusion and/or exclusion of a respective characteristic. Here, an inclusion region 1330 is provided for defining one or more inclusive user defined logic gates based on a plurality of characteristics 224 and an exclusion region 1340 for defining one or more exclusive user defined logic gates based on the plurality of characteristics 224. In the present embodiment, the inclusion region 1330 and the exclusion region 13340 include the same plurality of characteristics 224. In other embodiments, the inclusion region includes a first plurality of characteristics 224-1 and the exclusion region includes a second plurality of characteristics 224-2 different from the first plurality of characteristics 224-1.

[00168] In some embodiments, the inclusion region 1330 and/or the exclusion region 1340 includes one or more categorizations of a plurality of characteristics 224 for a user to select. For instance, in some embodiments, the one or more categorizations of the plurality of characteristics 224 include a first categorization of age, a second categorization of gender, a third categorization of race, a fourth categorization of treatments ( e.g ., one or more classes of pharmaceutical compositions), a fifth categorization of diagnostic and statistical medical codes, or a combination thereof. Thus, in accordance with a selection of a respective categorization, such as the third categorization of race, a respective plurality of characteristics 224 is displayed (e.g., display 308 of Figure 3) for further selection (e.g., first racial characteristic 224-1 of “African American,” second racial characteristics 224-2 of “Asian,” . . . , racial characteristic T 224-T of Figure 13). [00169] As another non-limiting example, in some embodiments, the fourth categorization of treatments provided to a user allows for granular selection and manipulation of user defined logic gate computational models 232 based on a respective class of a pharmaceutical composition or a respective pharmaceutical composition. Accordingly, in some embodiments, one or more tables describing a respective exposure of a pharmaceutical composition. However, the present disclosure is not limited thereto.

[00170] In some embodiments, the user interface 1300 displays a defined logic region 1350 that displays one or more user defined logics selected by a respective user. In the illustrated embodiment, a user has selected a first user defined logic gate that is inclusive of retained data elements associated with a first female characteristic 224-1, a second user defined logic gate that is inclusive of retained data elements associated with a second age characteristic 224-2 in a range in between 17 years of age and 65 years of age, and a third user defined logic cate that requires satisfying the first user defined logic gate and the second user defined logic gates. Accordingly, a corresponding computational tool is defined collectively by the first user defined logic gate, the second user defined logic gate, and the third user defined logic gate. As another non-limiting example, in some embodiments, a user provides a user defined data source tool 236 ( e.g ., conduct analysis on data elements associated a student employment status).

[00171] In some embodiments, the plurality of user-defined models associated with one or more computational models 323 includes one or more user-defined data formatting models that describe a formatting of a data element (e.g., describe a format 222 of formatting module 220 of Figure 2, block 416 of Figure 4A, etc.). In some embodiments, the one or more user-defined data formatting models includes formatting from a first user defined format 222-1 to a second user defined format 222-2. As a non-limiting example, in some embodiments, the one or more user-defined data formatting models includes user-defined JAVA type casting, such as a user defined widening casting and/or narrowing casting.

[00172] In some embodiments, the plurality of user-defined models associated with one or more computational models 323 includes one or more user-defined derivation models, such as a derivation model to derive a user defined hyperspace format 222.

[00173] Block 530 and block 532. Referring to block 530 of Figure 5C, in some embodiments, the plurality of computational tools 232 includes a plurality of visualization computational tools 232. Each visualization computational tool 232 includes a unique mechanism that allows the analysis to provide a corresponding visualization of the analysis, such as an output of an equation. For instance, referring briefly to Figure 8, a visualization listing 840 is provided through a graphical user interface 800. The visualization listing 840 describes a first visualization computational tool 232-1 associated with forming a respective table when outputting an analysis of a candidate subject. However, the present disclosure is not limited thereto. For instance, in some embodiments, the plurality of visualization computational tools 232 include one or more graphical chart visualization tools 232, such as a first pie-chart graphical visualization computational tool 232-1, a second histogram graphical visualization computational tool 232-2, a third area chart graphical visualization computational tool 232-3, a fourth scatter plot graphical visualization computational tool 232-4, a fifth linear graphical visualization computational tool 232-5, a sixth logarithmic graphical visualization computational tool 232-6, and the like. In some embodiments, the plurality of visualization computational tools 232 include one or more table visualization tools 232 (e.g., fifth table visualization computational tool 232-5 of Figure 8) that provides a visualization of an analysis using a first number of columns and a second number of rows, and one or more optional headers. In some embodiments, one of the first number or the second number is greater than one. [00174] Referring briefly to Figure 11, a graphical user interface 1100 is provide that depicts a result of providing an analysis of a candidate subject ( e.g ., a portion of a database entitled “mininc vl” utilizing one or more visualization computational tools 232, in accordance with an embodiment of the present disclosure. In some embodiments, the graphical user interface provides a candidate subject listing 1120. Additionally, the graphical user interface 1100 includes a summary tab 1180 that displays the visualization of the analysis in a graphical representation. Furthermore, in some embodiments, the graphical user interface 1100 includes a data tab 1190 that textually displays the retrieved data elements utilized to conduct the analysis (e.g., data tab 1190 of Figure 12). Specifically, in Figure 11, a visualization of a respect for an analysis of a candidate subject database is provided. The user has selected at least a first data source tool 236-1 associated with a first visit type (e.g., number of visits) characteristic 224-1, a second gender characteristic 224-2, and a third race characteristic 224. Additionally, the user has selected at least a first graphical visualization computational tool 232-1 to provide a first chart describing Block 534 and block 536. Referring to block 534 and block 536, in some embodiments the plurality of characteristics 224 (e.g., characteristics 224 of Figure 2, characteristics 224 of block 404 through block 414 of Figure 4A) includes a plurality of qualitative characteristics 224, a plurality of quantitative characteristics 224, or both. By way of example, in some embodiments, the plurality of characteristics 224 includes a first characteristic 224-1 associated with an age of a respective patient, a second characteristic 224-2 associated a gender of the respective patient, a third characteristic 224-3 associated with an ethnicity of the respective patient, a fourth characteristic 224-4 associated with a weight of the respective patient, a fifth characteristic 224-5 associated with a height of the respective patient, a sixth characteristic 224-6 associated with a diagnostic information of the respective patient, a seventh characteristic 224-7 associated with a pharmaceutical composition information of the respective patient, an eighth characteristic 224-8 associated with a treatment information of the respective patient, a ninth characteristic 224-9 associated with a site identification of the respective patient, a tenth characteristic 224-10 associated with a baseline year of the respective patient, an eleventh characteristic 224-11 associated with a physical behavioral information of the respective patient, a twelfth characteristic 224-12 associated with an emotional behavioral information of the respective patient, a thirteenth characteristic 224-13 associated with a mental health information of the respective patient, a fourteenth characteristic 224-14 associated with a visit type of the respective patient, a fifteenth characteristic 224-15 associated with a severity of illness scale of the respective patient, or a combination thereof. However, the present disclosure is not limited thereto. [00175] Block 538 and block 540. Referring to block 538 and block 540, the method 500 includes providing an analysis of the plurality of data elements to the remote client device 300 that is based on the selection by the user of the respective tool 232. For instance, in some embodiments, the analysis is provided in the form a report, such as a corresponding report provided through a report tab 1070 of Figure 10.

[00176] Referring briefly to Figure 10, a graphical user interface 1000 is provided that displays features for generating and displaying a report of an analysis. In some embodiments, a report tab 1070 generates a report of the analysis requested by the user, such as generating an electronic file including the report, such as a portable document file (PDF) or word document file. However, the present disclosure is not limited thereto. In some embodiments, the report provided by the report tab 1070 is displayed through the graphical user interface 1000. In some embodiments, the report includes a first listing 1010 that provides an identification of the request ( e.g ., request entitled “1.1 count with mapping table”). In some embodiments, the report includes a results listing 1020 that provides a status of the analysis, such as a plurality of information on a status of processing of the data elements retrieved for the analysis (e.g., successfully parsed, incomplete analysis, error retrieving data elements, etc.). In some embodiments, the report includes one or more visualizations of an analysis determined by a user selection of one or more corresponding visualization computational tools 232 and/or predetermined visualization computational tools 232. By way of example, in the graphical user interface 1000, a first table 1040 provides a first result of an analysis associated with a first number of visits characteristic 224-1, a second gender characteristic 224-2, and a third temporal characteristic 224-3, and a second table 1040 provides a second result of the analysis associated with the first number of visits characteristic 224-1 and the third temporal characteristic 224-3. In this way, in some embodiments, the method 500 includes displaying, on a display of the client device 300 (e.g., display 308 of Figure 3), a visualization of the analysis of the plurality of data elements that is based on a determination that the user selection a corresponding visualization computational tool 232 in the plurality of visualization computational tools 232.

[00177] Now that details of a method 500 for facilitating an analysis of a candidate subject have been disclosed, details regarding displaying (e.g., display 208 of Figure 2, display 308 of Figure 3, etc.) a plurality of graphical user interfaces (e.g., graphical user interface 600 of Figure 6, graphical user interface 700 of Figure 7, graphical user interface 800 of Figure 8, graphical user interface 900 of Figure 9, graphical user interface 1000 of Figure 10, graphical user interface 1100 of Figure 11, graphical user interface 1200 of Figure 12, graphical user interface 1300 of Figure 13, etc.), in accordance with an embodiment of the present disclosure, are disclosed with reference to Figure 6 through Figure 13.

[00178] Referring to Figure 6 through Figure 13, a variety of graphical user interfaces of a client application ( e.g ., client application 320 of Figure 3) are provided, in accordance to an exemplary embodiment of the present disclosure. The GUIs in Figure 6 through Figure 13 are used to illustrate a variety of the processes described throughout the present disclosure (e.g., method 400 of Figures 4A and 4B, method 500 of Figures 5A through 5C, etc.). For sake of clarity, Figure 6 through Figure 13 simply show the display (e.g., an output of display 308 of Figure 3) of a client device 300 without showing other details of the client device 300, such as input 310. In some embodiments, the GUIs in Figure 6 through Figure 13 include the following elements, or a subset or superset thereof: signal strength indicator(s) for wireless communications, such as cellular and Wi-Fi signals; time; a Bluetooth indicator; and a battery status indicator. These well-known elements are not described in detail so as not to unnecessarily obscure aspects of the disclosed embodiments. Furthermore, in some embodiments, one or more aspects of the illustrated GUI’s of Figures 6 through 13 form a dedicated user interface or client application 320.

[00179] In some embodiments, the graphical user interfaces of Figure 6 through Figure 13 are configured for use on a desktop type of a user device (e.g., a desktop computer system, etc.) and the like. However, the present disclosure is not limited thereto. For instance, in some embodiments, the graphical user interfaces of Figure 6 through Figure 13 are configured for use on a mobile type of a user device 300 (e.g., a smart phone, a tablet device, a wearable device, etc.),

[00180] Referring to Figure 6, a graphical user interface (GUI) 600 for retaining and analyzing health information is provided, in accordance with an embodiment of the present disclosure. Specifically, the GUI 600 of Figure 6 provides one or more mechanisms for requesting an analysis of a candidate subject and/or visualizing health information retained by the analysis system 200.

[00181] A first mechanism 610 allows a user to create a project 660 associated with a request for an analysis of a candidate subject using one or more tools (e.g., tool module 230 of Figure 2) through a command line interface (CLI), such as a through a console 920 of a graphical interface 900 of Figure 9. In some embodiments, the first mechanism 610 further allows a user to select a language of a project 660. In the present embodiment, a selected language is R. However, the present disclosure is not limited thereto. For instance, in some embodiments, the fist mechanism 610 allows a user to create a project 660 associated with a request for an analysis of a candidate subject using Python, JAVA, MATLAB, Excel, and the like. On the other hand, a second mechanism 620 allows the user to create the request for the analysis of the candidate subject through a graphical user interface, such as through a graphical user interface 700 of Figure 7. Thus, if a user is unskilled in utilizing a CLI, the second mechanism 620 provides a simpler process for conducting an analysis of a candidate subject. According, the analysis system 200 will provide a similar, or the same, analysis in if a user, through the first mechanism 610, creates a first project 660-1 associated with a first request for an analysis of a first candidate subject using a first tool 230-1 through a command line interface or, through the second mechanism 620, creates a second project 660-2 associated with the request for an analysis of the first candidate subject using the first tool 230-1 through a graphical user interface.

[00182] In some embodiment, the GUI 600 provides a listing 630 one or more projects 660 associated with a request for an analysis of a candidate subject. For instance, the listing 630 of Figure 6 provides a first sub-listing 640 associated with one or more projects 660 previously created through the first mechanism 610, such that the one or more projects 660 associated with the first sub-listing 640 include requests for analysis of candidate subjects in command line interfaces. The listing 630 of Figure 6 further provides a second sub-listing 650 associated with one or more projects 660 created through the second mechanism 620, such that the one or more projects 660 associated with the second sub-listing 650 include requests for analysis of candidate subjects in graphical user interfaces.

[00183] By providing the listing 630 of one or more projects 660, a user is provided opportunities to repeat a ( e.g ., communicate the same) request for an analysis of a candidate subject either using a previous selection of the retained data elements or a new selection of the retained data elements. By way of example, consider a first project 660-1 and a user that communicates a first request for an analysis of a candidate subject (e.g., female patients with stage II lung cancer) on January 01, 2000 that utilizes a first computational tool 236-1 in coordination with a first data source tool 236-1 that retrieves data elements associated with a first characteristic 224-1 of a female patient and a second characteristic 224-2 of a positive indication of stage II lung cancer. In response, the analysis system 200 returns a first analysis based on the selection of the first computational tool 236-1 in coordination with the first data source tool 236- 1 that is based on the data elements retained by the analysis system to date. At a later date of August 01, 2010, the user can utilize the first project 660-1 to communicate a second request for an analysis of the candidate subject (e.g., female patients with stage II lung cancer) that utilizes the first computational tool 236-1 in coordination with the first data source tool 236-1 based on any one of the data elements retained by the analysis system to as of a previous date (e.g., date of creation of the first project 660-1, date of the first request, etc). However, the present disclosure is not limited thereto. For instance, in some embodiments, a second data source tool 236-2 provides this temporal characteristic 224. For instance, in the present example, the second data source tool 236-2 provides a third characteristic 224-3 in the first requests that defines a limit or range of dates used in the analysis of the first candidate subject ( e.g ., all data elements having a respective characteristic 224 associated with a date prior to and/or including January 01, 2020; prior to and/or including August 01, 2010; etc.).

[00184] Referring to Figure 7, the graphical user interface 700 provides a first mechanism 710 that initiates a request for an analysis of a candidate subject based on the graphically defined request provided by a user. In some embodiments, the graphical user interface 700 further provides a second mechanism 720 that interrupts the request for the analysis of the candidate subject.

[00185] Referring to Figures 8, a graphical user interface 800 is provided that displays at least one first listing of a plurality of characteristics 224 associated with a candidate subject and at least one second listing of a plurality of tools 226 available to select when requesting an analysis of the candidate subject. In some embodiments, the at least one second listing of the plurality of tools 226 includes a logic gate listing 820 that displays one or more logic gate computational tools 232, a time reduction 830 listing that displays one or more temporal computational tools 232, a visualization listing 840 that displays one or more visualization computational tools 232, a type transformation listing 850 that displays one or more computational tools 232, a statistical analysis listing 860 that displays one or more statistical model computational tools 232 (e.g., chi square test computational tool 232-8), a time filter listing 870 that displays one or more temporal data source tools 236, or a combination thereof.

[00186] Figure 9 provides a further illustration of the graphical user interface 900 for retaining and analyzing health information, in accordance with an embodiment of the present disclosure. Specifically, Figure 9 shows the graphical user interface 900 of a request for an analysis compiled in a command line interface, such as R language. However, the present disclosure is not limited thereto. In some embodiments, the graphical user interface 900 includes a script region 910 to reading and/or writing one or more scripts associated with the request for the analysis. Moreover, the graphical user interface 900 includes a console window 920 for inputting at least a portion of the request for the analysis of the candidate subject [00187] In some embodiments, the graphical user interface 900 further includes a dataset tab 950 for viewing the plurality of data elements retrieved by or accessible to the analysis system 200. In some embodiments, the user selects the candidate subject and/or views information about the candidate subject through the dataset tab 950. However, the present disclosure is not limited thereto. For instance, in some embodiments, the dataset tab 950 includes information about the data elements ( e.g ., received by the analysis system 200 and/or normalized by the analysis system 200) associated with the candidate subject, such as if the data elements include PII, a source of the data elements, a count of a number of patients in a plurality patients associated with the data elements, and the like. In some embodiments, the graphical user interface 900 further includes a category tab 952 that stores all or some of values that are present as factors and/or character vectors of the data elements. However, the present disclosure is not limited thereto. In some embodiments, the graphical user interface 900 further includes a mapping tab 954 that allows the user to create a mapping, such as a demographic characteristic 224 map. In some embodiments, the data being mapped includes geolocation information. In some embodiments, the graphical user interface 900 further includes a packages tab 956 that includes a collection of one or more functions (e.g., scripts, user-defined computational tools 232, etc.) that is compiled. However, the present disclosure is not limited thereto.

[00188] Figure 10 provides another further illustration of a graphical user interface 1000 for retaining and analyzing health information, in accordance with an embodiment of the present disclosure. Specifically, Figure 10 illustrates the graphical user interface 1000 in which one or more aggregated results provided in response to a request for an analysis of a candidate subject, such as one or more tables, one or more graphs, and/or one or more other visualizations provided by a respective visualization computational tool 232 is viewed and/or described textually. In some embodiments, the graphical user interface 1000 includes a project tab 1050 that allows for selecting and utilizing one or more prior request for an analysis of a candidate subject that is saved for future (e.g., current) use by a user.

[00189] Figure 12 provides an illustration of a graphical user interface 1200 for retaining and analyzing health information, in accordance with an embodiment of the present disclosure. Specifically, the graphical user interface 1200 displays a first table 1230-1 from a plurality of tables 1230 available from the selection of a data tab 1190 (e.g., data tab 1190 of Figure 11). Each table 1230 includes a subset of a plurality of data elements and a corresponding plurality of characteristics 224 associated with the plurality of data elements. In some embodiments, the table 1230 provides the plurality of data elements received by the analysis 200. In some embodiments, the table 130 provides the plurality of normalized data elements retained by the analysis system 200. For instance, the first table 1230-1 of the graphical user interface 1200 is associated with a subset of data elements further associated with a drug exposure characteristic 224, and provides a table of each other characteristic 224 associated with each data element in the subset of data elements.

[00190] The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teachings. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and their practical application, to thereby enable others skilled in the art to make and utilize various exemplary embodiments of the present invention, as well as various alternatives and modifications thereof. It is intended that the scope of the invention be defined by the Claims appended hereto and their equivalents.

REFERENCES CITED AND ALTERNATIVE EMBODIMENTS

[00191] All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes

[00192] The present invention can be implemented as a computer program product that includes a computer program mechanism embedded in a non-transitory computer-readable storage medium. For instance, the computer program product could contain instructions for operating the user interfaces described with respect to Figures 2, 3, 6, 7, 8, 9, 10, 11, 12, and 13. These program modules can be stored on a CD-ROM, DVD, magnetic disk storage product, USB key, or any other non-transitory computer readable data or program storage product.

[00193] Many modifications and variations of this invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. The specific embodiments described herein are offered by way of example only. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated. The invention is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled.