Computer-implemented dashboards providing dynamic digital healthcare data

Abstract

A computer-implemented dashboard for providing digital healthcare data is generated by a computer system as a user interface for display on a display device operatively coupled to the computer system. The computer system generates a first plot including genetic and / or epigenetic and / or other marker test results for a patient determined by conducting genetic and / or epigenetic and / or other marker tests on genetic and / or epigenetic and / or other markers obtained from the patient to assess the presence of disease in the patient. Overlaid on the plot are genetic and / or epigenetic and / or other marker test results for a patient population to which the patient belongs. The genetic and / or epigenetic and / or other marker test results for the patient population are obtained from a cohort test of genetic and / or epigenetic and / or other marker tests administered to the patients. The user interface is displayed on the display device. The first plot is displayed within the user interface. TIFF2026508388000007.tif53128

Description

[Technical Field]

[0001] The present disclosure relates to computer-implemented methods, computer-readable media and computer systems for generating computational resources directed to health, wellness and other data of an individual, such as a patient, and displaying the generated computational resources on a display device for consumption by the patient or otherwise. [Background technology]

[0002] background Patients, healthcare providers (e.g., clinicians, physicians), employers, health plans, life insurance companies, and drug developers (e.g., pharmaceutical companies) each consume information that may differ based on their particular needs and circumstances. Often, an individual viewing digital data, for example, on a display device of a computer system, is interested in obtaining comparative information related to the data and / or more details to enable the individual to make informed decisions about the underlying medical problem the data addresses, priorities, costs, risks and risk mitigation measures, management, intervention (e.g., treatment), and monitoring. Summary of the Invention

[0003] overview This specification describes techniques related to computer-implemented methods, computer-readable media, and computer systems directed to generating and displaying dashboards to provide digital data.

[0004] In one aspect, a method is provided that includes generating, by a computer system, a user interface for display on a display device operatively coupled to the computer system; generating, by the computer system, first data including test results of genetic biomarkers, epigenetic biomarkers, and / or other biomarkers for the patient to assess the presence or absence of a disease, disorder, or risk in the patient; displaying the user interface on the display device; and displaying the first data in the user interface.

[0005] In some embodiments, the data is selected from measurements (e.g., weight, blood pressure, etc.), test results (e.g., genetic assays, epigenetic assays, protein assays, metabolic assays, etc.), monetary (e.g., cost of procedures, cost of medications, claims costs, etc.), industrial / prevalence (e.g., prevalence of heart disease in a particular geographic area, number of heart attacks per year among truck drivers, etc.), interventions (e.g., surgery, etc.), medical (e.g., ICD10 codes, medication use, etc.), clinical (e.g., blood pressure, etc.), treatments (e.g., cost of statins, drugs that reduce inflammation, etc.), lifestyle (e.g., smoking, alcohol consumption, frequency of exercise, etc.), supplement use (e.g., herbs, vitamin D, vitamin B12, iron, etc.), electronic data (e.g., imaging, electronic health records, publicly available data (e.g., repositories, etc.), publications, formulations, clinical trials, etc.) and their diagnostic, interventional, management, monitoring and / or therapeutic significance. In some aspects, the data includes standalone data, aggregated data, complementary data, derived data, numerical data, textual data, image data, and combinations thereof.

[0006] In some embodiments, the data is displayed in a user interface in one or more plots. In some embodiments, the data is compared to corresponding genetic, epigenetic, and / or other biomarker test results from a cohort population. In some embodiments, the data is overlaid on the genetic, epigenetic, and / or other biomarker test results for the population.

[0007] In some embodiments, the user interface displays diagnostic information, prognostic information, uncertainty quantification, clinical and / or business risk management or decision making, regulatory and / or policy making, intervention, monitoring and / or treatment decisions and strategies for a patient or group of patients or a user or group of users.

[0008] In some embodiments, the genetic, epigenetic, and / or other biomarker test results for a patient population comprise a bar graph showing the age of the patient population on the X-axis and the probability that patients of different ages have the disease, disorder, or risk on the Y-axis, where the test results for the patients comprise the probability that the patient has the disease.

[0009] In some embodiments, the patient is age-related, and the method further comprises displaying the probability that the patient has the disease adjacent to the probability for a population of patients of the same age as the patient.

[0010] In some embodiments, the patient is associated with a gender, and the method further comprises displaying the probability that the patient has the disease adjacent to the probability for a population of patients of the same gender as the patient.

[0011] In some embodiments, the method further includes receiving a ranking of the contributions of a plurality of genetic biomarkers, epigenetic biomarkers, and / or other biomarkers, including genetic biomarkers, epigenetic biomarkers, and / or other biomarkers, to the presence of a disease in a patient; generating a chart including names of the plurality of markers on an X-axis and a normalized ranking of the contributions of the plurality of genetic biomarkers, epigenetic biomarkers, and other biomarkers for the patient; overlaying the chart with another chart showing the normalized ranking of the contributions of the plurality of genetic biomarkers, epigenetic biomarkers, and / or other biomarkers to the patient population, resulting in a second plot of the rankings for the markers; and displaying the second plot adjacent to the plot in a user interface.

[0012] In some embodiments, the method further includes generating a plot of the distribution of measurements of the genetic biomarkers, epigenetic biomarkers, and / or other biomarkers in relation to a particular genetic biomarker, epigenetic biomarker, and / or other biomarker; overlaying the measurements of the genetic biomarkers, epigenetic biomarkers, and / or other biomarkers measured for the patient on the plot; and displaying the plot together with the overlaid measurements of the genetic biomarkers, epigenetic biomarkers, and / or other biomarkers in a user interface.

[0013] In some embodiments, the method further includes determining upper and lower uncertainty limits associated with the genetic, epigenetic, and / or other biomarker measurements measured for the patient; and displaying within the plot the upper and lower limits bounding the genetic, epigenetic, and / or other marker measurements.

[0014] In some embodiments, the method further includes detecting a selection of data displayed within the user interface; in response to detecting the selection, displaying a window adjacent to the data within the user interface; and displaying a hyperlink to further information within the window.

[0015] In some embodiments, the additional information includes literature regarding test results for genetic biomarkers, epigenetic biomarkers, and / or other biomarkers, or literature regarding the disease, disorder, or risk.

[0016] In some embodiments, the literature is selected from publications, clinical data, clinical trials, and formulations.

[0017] In yet another aspect, a computer-readable medium is provided that stores computer instructions that, when executed by one or more computer processors, are configured to cause the one or more computer processors to perform operations including any of the methods described herein.

[0018] In yet another aspect, a computer system is provided that includes one or more computer processors; and a computer-readable medium storing computer instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including any of the methods described herein.

[0019] As used herein, the term "genome" refers to the entire genetic information of an organism, as encoded in its primary DNA sequence. A genome includes both genes and non-coding sequences. For example, a genome may refer to a microbial genome or a mammalian genome.

[0020] "Nucleic acid," "oligonucleotide," and "polynucleotide" refer to deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) and polymers thereof in either single- or double-stranded form. Unless otherwise limited, the terms encompass nucleic acids containing known analogues of natural nucleotides that have similar binding properties as the reference nucleic acid. The term nucleic acid is used interchangeably with genetic material, cDNA, mRNA, and gene.

[0021] Reference to a "DNA region" should be understood as a reference to a specific section of DNA. These DNA regions are identified either by gene name, by reference to a set of chromosomal coordinates, or by reference to single nucleotide polymorphisms (SNPs). Both chromosomal coordinates and genes or genomic regions are well known and understood by those skilled in the art. Generally, a gene or genomic region can be routinely identified by reference to its name (through which both its sequence and its location on a chromosome can be routinely obtained), by reference to its chromosomal coordinates (through which the name of the gene or genomic region and its sequence can also be routinely obtained), or by sequence (through which both the gene and genomic region can be routinely obtained).

[0022] Reference to each gene / DNA region detailed herein should be understood as a reference to all forms of these molecules and fragments or variants thereof. As will be recognized by those skilled in the art, some genes are known to exhibit allelic variations. Allelic variations include single nucleotide polymorphisms, various sizes of insertions and deletions, and simple repeat sequences such as dinucleotide and trinucleotide repeats. Variants include nucleic acid sequences from the same region that share at least 90%, 95%, 98%, or 99% sequence identity with the DNA regions described herein, i.e., have one or more deletions, additions, substitutions, inversions, etc. Therefore, the compositions and methods described herein should be understood to cover such variants. Therefore, the compositions and methods described herein should be understood to cover all forms of DNA resulting from any other mutations, polymorphisms, or allelic variations.

[0023] As used herein, the term "sequencing" refers to a sequencing method for determining the precise order of nucleotide bases (e.g., adenine, guanine, cytosine, and thymine) in a nucleic acid molecule (e.g., a DNA or RNA nucleic acid molecule). It includes any method or technique used to determine the order of the four bases in a DNA strand.

[0024] The term "barcode," as used herein, refers to any unique, non-naturally occurring nucleic acid sequence that can be used to identify a nucleic acid molecule.

[0025] Real-time, derived, and predictive data (e.g., collected from samples, electronic health records, devices, etc.) can be collected and stored, thus becoming historical data for ongoing or future decision-making for a process, setting, or application.

[0026] A "computer-readable medium" is an information storage medium that can be accessed by a computer using a commercially available or custom interface. Exemplary computer-readable media include memory (e.g., RAM, ROM, flash memory, etc.), optical storage media (e.g., CD-ROM), magnetic storage media (e.g., computer hard drives, floppy disks, etc.), punch cards, or other commercially available media. Information can be transferred between computer systems and media, between computer systems, or between computer systems and computer-readable media for storage or access of the stored information. Such transmission can be electrical or by other available methods, such as an IR link, wireless connection, etc.

[0027] When a range of values ​​is provided, it is understood that each intervening value, the upper and lower limits of the range, and any other stated or intervening value within the stated range are encompassed herein. It is recognized that test results, data, measurements, and biomarkers (or markers) can be used interchangeably. For example, measurements can be measured or obtained from a patient (e.g., genetic biomarkers, epigenetic biomarkers, inflammatory biomarkers, lipids, weight, etc.). For example, biomarkers can be measured (e.g., methylation percentage) or expressed using binary data (e.g., yes or no; presence or absence; adenosine (A) or guanine (G)). For example, data can include, for example, test results, biomarker information (e.g., measured or binary), lifestyle information (e.g., lifestyle habits (e.g., smoker, drinker), medication or supplement use, etc.), and / or one or more measurements from any number of other sources (e.g., public databases, publications, insurance claims, demographics, costs or cost estimates, etc.).

[0028] The details of one or more implementations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, drawings, and claims. [Brief explanation of the drawings]

[0029] [Figure 1] FIG. 1 is a schematic diagram of a computer system configured to display a dashboard as described in this disclosure. [Figure 2] 1 is an exemplary user interface that can be displayed on a display device. [Figure 3] 1 is an exemplary user interface that can be displayed on a display device. [Figure 4] 1 is an exemplary user interface that can be displayed on a display device. [Figure 5] 1 is an exemplary user interface that can be displayed on a display device. [Figure 6-1] 1 is an exemplary user interface that can be displayed on a display device. [Figure 6-2] This is a continuation of Figure 6-1. [Figure 7] 1 is an exemplary user interface that can be displayed on a display device. [Figure 8] 1 is an exemplary user interface that can be displayed on a display device. [Figure 9] 1 is an exemplary user interface that can be displayed on a display device. [Figure 10] 1 is an exemplary user interface that can be displayed on a display device. [Figure 11] 1 is an exemplary user interface that can be displayed on a display device. [Figure 12] 1 is an exemplary user interface that can be displayed on a display device. [Figure 13] 1 is an exemplary user interface that can be displayed on a display device. [Figure 14] 1 is an exemplary user interface that can be displayed on a display device. [Figure 15] 1 is an exemplary user interface that can be displayed on a display device. [Figure 16] 1 is an exemplary user interface that can be displayed on a display device. [Figure 17] 1 is an exemplary user interface that can be displayed on a display device. [Figure 18] 1 is an exemplary user interface that can be displayed on a display device. [Figure 19] 1 is an exemplary user interface that can be displayed on a display device. [Figure 20] FIG. 1 is a block diagram illustrating an example of a computer system that may be used to provide computational functionality associated with the described algorithms, methods, functions, processes, flows, and procedures as described in this disclosure in accordance with some implementations of the present disclosure.

[0030] Like reference numbers and designations in the various drawings indicate like elements. DETAILED DESCRIPTION OF THE INVENTION

[0031] Detailed Description The present disclosure provides a method for an individual (e.g., patient), a clinician (e.g., doctor, nurse practitioner), or another user (e.g., pharmacist, researcher, employer, insurer) to collect and use data such as measurements (e.g., weight, blood pressure, etc.), test results (e.g., genetic assays, epigenetic assays, protein assays, metabolic assays, etc.), monetary (e.g., procedure costs, medication costs, claims costs, etc.), industry / prevalence (e.g., heart disease prevalence in a particular geographic area, number of heart attacks per year among truck drivers, etc.), interventions (e.g., surgical procedures, etc.), medical (e.g., ICD10 codes, medication use, etc.), and other relevant information. Describe a platform that can receive and understand standalone or aggregated information regarding diagnostic, interventional, management, monitoring, and / or their therapeutic implications, including clinical (e.g., blood pressure, etc.), therapeutic (e.g., cost of statins, drugs that reduce inflammation, etc.), lifestyle (e.g., smoking, alcohol consumption, frequency of exercise, etc.), supplement use (e.g., herbs, vitamin D, vitamin B12, iron, etc.), electronic data (e.g., imaging, electronic health records, publicly available data (e.g., repositories, etc.), publications, formulations, clinical trials, etc.).

[0032] The platform can provide diagnostic information, prognostic information, associated risk factors, uncertainty quantification, etc. The platform can provide metrics such as measurements (e.g., weight, blood pressure, etc.), tests (e.g., genetic assays, epigenetic assays, protein assays, metabolic assays, etc.), monetary (e.g., procedure cost, medication cost, claims, etc.), industrial / prevalence (e.g., heart disease prevalence in a particular geographic area, number of heart attacks per year among truck drivers, etc.), interventions (e.g., surgery, etc.), medical (e.g., ICD10 code, medication use, etc.), clinical (e.g., blood pressure, etc.), treatments (e.g., statin cost, drugs that reduce inflammation, etc.), lifestyle (e.g., smoking, alcohol consumption, exercise frequency, etc.), supplements (e.g., herbal , vitamin D, vitamin B12, iron, etc.) and / or additional data (imaging, electronic health records, publicly available data (e.g., repositories, etc.), publications, formulations, clinical trials, etc.) that may assist a physician or other in understanding and implementing, for example, diagnostic, clinical and / or business risk management or decision-making, regulatory and / or policy development, intervention, monitoring and / or treatment decisions and strategies for a patient or group of patients or a user or group of users. Additionally, the platform may provide the ability to use artificial intelligence or other algorithms (e.g., large-scale language models) to assist in mining, exploring, interpreting, collating, communicating, and providing additional information (e.g., journal articles, clinical guidelines, raw or transformed data, etc.). While "patient" is often used throughout this disclosure, this term is not limited to individuals previously diagnosed with a disease or disorder, nor is it intended to be mutually exclusive with "stakeholder" or "user."

[0033] The compositions and methods described herein can be used to provide changes in management, outcomes (e.g., health, cost, etc.) or other decisions for users (e.g., clinicians, payers, employers, researchers, insurers, governments, etc.) for patients or groups of patients or users with or without a disease, disorder, or risk. Although the compositions and methods described herein are discussed in relation to cardiovascular disease (e.g., coronary heart disease, stroke, etc.) and cardiovascular risk factors or comorbidities (e.g., obesity, smoking, alcohol consumption, etc.), the compositions and methods described herein can be similarly applied in relation to cardiometabolic disorders (e.g., hypertension, high lipoprotein, etc.), insulin-related health conditions (e.g., prediabetes, type 2 diabetes, etc.); dementia (e.g., Alzheimer's disease, etc.), cerebrovascular disease, mortality, cancer (e.g., breast cancer, etc.), other complex disorders or diseases (e.g., autism, mental health, COPD, renal disease, etc.), and any number of other diseases, disorders, or risks.

[0034] 1 is a schematic diagram of a computer system 100 configured to execute and display the platform described in this disclosure. Computer system 100 includes one or more processors and a computer-readable medium (e.g., a non-transitory computer-readable medium) storing computer instructions that, when executed by the one or more processors, can perform the operations described in this disclosure. Computer system 100 can generate a "dashboard" comprised of one or more user interfaces (see, e.g., FIGS. 2-19) for display on a display device 105 operatively coupled to computer system 100.

[0035] As described herein, genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein for a patient, a population of patients, or a user can be represented and displayed (e.g., graphically, textually) by computer system 100 via any number of user interfaces on display device 105. For example, genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein for a population to which a patient belongs (e.g., a patient population) (e.g., based on age, sex, race, ethnicity, etc.) can be represented and depicted in standalone or aggregated form for the patient alone, for a non-patient user (e.g., clinician, technician, etc.), or compared to a population (e.g., a cohort of members). The same genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results and / or data described herein can be obtained for a population, and the genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results and / or data described herein for a population can be grouped into cohorts of individuals within the same or different demographics (e.g., age, sex, race, ethnicity, etc.) of individuals (e.g., patients or non-patients).As used herein, test result or measurement refers, without limitation, to results or data obtained from genetic and / or epigenetic testing, but can also refer to the presence, absence, or amount of a protein, feature detection during imaging, blood pressure, lifestyle (e.g., smoking, alcohol consumption, etc.), results from metabolic assays, blood test results, financial (e.g., cost of a procedure, cost of medication, claims, etc.), industry / prevalence (e.g., heart disease prevalence in a particular geographic area, number of heart attacks per year among truck drivers, etc.), intervention (e.g., surgery, etc.), medical (e.g., ICD10 code, medication use, etc.), clinical (e.g., blood pressure, etc.), treatment (e.g., cost of statins, drugs that reduce inflammation, etc.), supplements (e.g., herbs, vitamin D, vitamin B12, iron, etc.), and / or additional data (electronic health records, publicly available data (e.g., repositories, etc.), publications, formulations, clinical trials, etc.).

[0036] As described herein, the compositions and methods involve processing a variety of different types of data (e.g., lifestyle habits, medical history, previous and current diagnoses (e.g., according to the International Classification of Diseases, Tenth Revision (ICD10)), symptom occurrence (e.g., by age, by sex, etc.)), which can be used to train, test, or validate machine learning or other algorithms for risk assessment or quality control, or to create training, test validation datasets and / or iterative training for machine learning. Additionally, in some embodiments, the compositions and methods described herein are capable of rapid and / or high-throughput processing of samples and data generated from sample processing, which can be used for the rapid generation of personalized care / intervention / management / monitoring / treatment program components.

[0037] User Interface Examples of user interfaces that may be displayed are shown in Figures 2-19, but are not intended to be limiting. Computer system 100 may display user interfaces in a static or dynamic format. Computer system 100 may display several different types of data (e.g., standalone, aggregated, implied, derived, numeric, text, image, or a combination thereof), plots, test results, publications, or hyperlinks to any of the foregoing within one or more user interfaces to assess the presence or absence of a disease or disorder or risk in a patient or population. Using the input device 110, a user can interact with the plot in the user interface, for example, to view different items of information contained in the plot (e.g., drop-down menus, embedded links, etc.); drill down to results, data, methods, analyses, and / or interpretations; overlay or superimpose data, test results, etc. for a patient or user or group of patients or users with corresponding data, test results, etc. for a population; and / or use artificial intelligence or other algorithms (e.g., large-scale language models) to search, interpret, collate, communicate, and provide additional information (e.g., journal articles, clinical guidelines, raw or converted data, etc.); and provide artificial intelligence assistants (e.g., large-scale language models) to provide references (e.g., journal articles, clinical guidelines), context, and / or query information, etc.

[0038] Using a user interface, described in more detail below, a patient, physician, or other individual or user or group of users can review patient information compared to the population as a whole or to populations grouped by age and / or sex or other confounding variables. Patient information may be provided as anonymized items. Additionally, users can compare population information as a whole or grouped by age and / or sex or other confounding variables. Methods for determining genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein for one or more patients or one or more users are known in the art, or can be entered into computer system 100 if that information is known for the patient or user.

[0039] The computer system 100 described herein can be configured to perform univariate biomarker analysis, which is primarily used to compare results for a user with data sources (e.g., external sources such as, but not limited to, third-party studies). The computer system 100 described herein can also be configured to perform multivariate risk and diagnostic predictions using multiple data sources (e.g., measurements, electronic health records, lifestyle habits, etc.). The system can perform both univariate and multivariate analyses on the same sources for a user.

[0040] 2A and 2B show exemplary user interfaces that may be displayed by a display device. In the user interfaces, computer system 100 may display patient information (e.g., patient ID, name, age, gender, etc.), user information (e.g., name, ID, and contact information of the physician or other user requesting and / or reviewing the information), and / or sample information (e.g., specimen type, date obtained from the patient, etc.). While "clinician" is used in both the user interfaces shown in FIGS. 2A and 2B, the same or different information may be similarly or differently configured in the user interfaces for any number of users as described herein.

[0041] 3A and 3B illustrate exemplary user interfaces that can be displayed by a display device. Different graphical formats can be used in the user interface to stratify patient information. For example, computer system 100 can generate a plot as a vertical bar graph showing the age of a patient population on the X-axis and the probability of patients of different ages having a disease or disorder on the Y-axis (FIG. 3A), or conversely, as a horizontal bar graph showing the probability (e.g., score) of patients of different ages having a disease or disorder on the X-axis and the age of the patient population on the Y-axis (FIG. 3B). Genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein for a patient can include probability values ​​that the patient has a disease or disorder (or does not have a disease or disorder), and computer system 100 can display the determined probabilities for the patient on a population probability bar graph. This allows a user to compare the patient's probability of having a disease or disorder with the age-related probability of the disease or disorder in the population.

[0042] In some implementations, the computer system 100 can generate a plot (e.g., a bar graph) showing the gender of a patient population on the X-axis and the probability that patients of the same gender have the disease on the Y-axis, where the gender shown can be selected to match the gender of the patient. The computer system 100 can display the determined probability for the patient relative to the population probability for individuals of the same gender as the patient. Similarly, the computer system 100 can display the determined probability for the patient relative to the population probability for individuals of the same race, ethnicity, or other characteristic as the patient.

[0043] 4 is an exemplary user interface that can be displayed by a display device. The user interface in FIG. 4 is a graph showing the risk of coronary heart disease (CHD) in male patients compared to a population of male cohorts. For example, if the disease, disorder, or risk is related to CHD, the cohort data can be obtained from the Framingham Heart Study Offspring Study, the Intermountain Healthcare (IM) Study, and / or the Iowa Study, which are well known in the art.

[0044] FIG. 5 is an exemplary user interface that can be displayed by a display device. In some implementations, results can be obtained from more than one (e.g., multiple) genetic biomarkers, epigenetic biomarkers, and / or other biomarkers from a patient. Different genetic biomarkers, epigenetic biomarkers, and / or other biomarkers can have different levels of contribution to the association or causation of a disease, disorder, symptom, condition, or risk in a patient. The contributions can be derived from a model or from other approaches, such as SHapley Additive Explanations. In some implementations, the computer system 100 receives a ranking of the contribution of each of multiple genetic markers to the presence of disease in a patient. The computer system 100 can generate a user interface that includes the name of each marker on the X-axis and the ranking or score (e.g., normalized between 0 and 1) of each biomarker's contribution to the disease, disorder, or risk. For example, the computer system 100 can generate a graph showing the biomarkers that contribute most to the disease or disorder or risk and the genetic markers that contribute least to the disease, disorder, or risk, with the remaining genetic markers in between sorted in descending order (FIG. 5). Generally, contributions can be compared to the population as a whole, or provided for a particular individual, or aggregated to groups regardless of age or sex. In some implementations, the computer system 100 can generate Table 1 listing the relevant genetic and / or epigenetic biomarkers displayed in a user interface (see below).

[0045] (Table 1) TIFF2026508388000002.tif65141

[0046] The computer system 100 overlays the genetic, epigenetic, and / or other marker test results for individual patients with the corresponding rankings of genetic and / or epigenetic and / or other marker tests determined for the entire population, and displays the resulting plot in a user interface.

[0047] The plots shown in the user interface show the aggregate contribution of each marker to the disease compared to the contribution in an individual patient. For example, if a first genetic and / or epigenetic and / or other marker (e.g., cg0) is ranked as having the greatest contribution to disease in the general population, but the same genetic and / or epigenetic and / or other marker has a relatively low contribution to disease in an individual patient, the physician will be notified that the first genetic and / or epigenetic and / or other marker may be less important for treating the individual patient. Conversely, if a different genetic and / or epigenetic and / or other marker (e.g., cg3) is ranked as having the lowest contribution to disease in the general population, but the same genetic and / or epigenetic and / or other marker has the highest contribution to disease in an individual patient, the physician is notified that the different genetic and / or epigenetic and / or other marker is likely to be most important for treating the individual patient. In this manner, risk contributions can be used to determine which markers significantly contribute to an individual patient's disease or condition or symptom.

[0048] As described herein, computer system 100 can display each of the normalized rankings as a selectable object. Using input device 115, a user can select a normalized ranking for one of the genetic biomarkers, epigenetic biomarkers, and / or other biomarkers. In response to detecting a selection (e.g., a mouse click, hover, touch input, or any other input), computer system 100 can display information about, for example, the genetic biomarkers, epigenetic biomarkers, and / or other biomarkers (e.g., hyperlinks, publications, clinical data, clinical trials, formulations, etc.).

[0049] 6 is another exemplary user interface that can be displayed by a display device. In some implementations, computer system 100 can generate a box plot of the distribution of genetic, epigenetic, and / or other biomarker measurements (e.g., for an entire patient population) associated with a particular genetic, epigenetic, and / or other biomarker. Computer system 100 can display the box plot with the genetic, epigenetic, and / or other biomarker measurements overlaid for the patient within the user interface. For example, computer system 100 can display the genetic, epigenetic, and / or other biomarker measurements for the patient as dotted lines over the box plot of the distribution of the genetic, epigenetic, and / or other marker measurements. The computer system 100 can similarly create and display multiple box plots for each genetic biomarker, epigenetic biomarker, and / or other biomarker, each plotted with the genetic biomarker, epigenetic biomarker, and / or other biomarker measurement for the patient. In some implementations, for each box plot, specifically for the genetic biomarker, epigenetic biomarker, and / or other biomarker measurement measured for the patient, the computer system 100 can determine upper and lower uncertainty bounds associated with the measurement. The computer system 100 can display the upper and lower bounds as solid lines on either side (e.g., above and below) of a dotted line representing the genetic biomarker, epigenetic biomarker, and / or other biomarker measurement for the patient. The computer system 100 can similarly create and display uncertainty bounds for multiple box plots corresponding to the distribution of measurements of multiple genetic biomarkers, epigenetic biomarkers, and / or other biomarkers.The user interface can be used to ensure the robustness of measurements and allow the user to compare measurements of individual patients with measurements of patient populations.

[0050] 7 is an exemplary user interface that can be displayed by a display device. In some implementations, computer system 100 can display a selectable object (e.g., a drop-down box) through which a user can select a particular marker (e.g., biomarker "rs1"). In response to a selection (e.g., using input device 115), computer system 100 can retrieve allele frequencies (e.g., from public or private databases) for different populations (e.g., European, Asian, East Asian, etc.) (see Table 2) and provide them as part of the user interface. Patients, physicians, or other users can interact with the user interface to learn additional context, for example, regarding ethnicity, age, gender, etc. Within the user interface, computer system 100 can also display publicly available genome-wide association study (GWAS) data, which can show whether independent studies have detected associations between biomarkers and risk factors and diseases, disorders, or risks.

[0051] (Table 2) TIFF2026508388000003.tif97128

[0052] FIG. 8 is an exemplary user interface that can be displayed by a display device. As shown in FIG. 8, the computer system 100 can generate cost analysis data (Y-axis) versus time (X-axis) for cardiovascular disease displayed based on various populations (hypertension, coronary heart disease, congestive heart failure, stroke, other cardiovascular disease, and disease types including hypertension) using national and regional data or statistics on prevalence, as well as other data or factors. As described herein, the data can be further aggregated, for example, based on demographic information (e.g., age, sex, geographic location, etc.). In some embodiments, the user interface can display cost analyses (e.g., cost minimization, cost-effectiveness, cost utility, cost benefit, cost-outcome, financial impact, information value, pharmacoeconomics, incremental cost-effectiveness ratio, etc.) that can include hyperlinks and / or test utilization information such as that shown in Table 3 below to further review the cost analysis data by month, year, employee population, tested population, etc. The cost analysis displayed in the user interface can aid in estimating treatment costs and outcomes at the individual, group of individuals, and / or population level.

[0053] (Table 3) Typical cost usage TIFF2026508388000004.tif28141

[0054] 9 is an exemplary user interface that computer system 100 may display on a display device that causes the user interface to display the prevalence of coronary heart disease in the American population based on age and sex. As described herein, the data used to generate such a user interface may be obtained from available national and regional statistics and may be displayed based on any number of factors (e.g., demographics; disease, disorder or risk; ethnicity; geography, institution, etc.).

[0055] Figure 10 is another exemplary user interface that may be displayed by a display device. Figure 10 shows an example in which coronary heart disease mortality rates are depicted geographically via a color-coded map based on age-standardized rates (per 100,000 people). As shown, Figure 10 is independent of race, ethnicity, and sex, although the user interface may be configured to display data in which the contribution of these factors is shown.

[0056] 11 is an exemplary user interface that can be displayed by a display device. In this example, uncertainty measurements (e.g., uncertainty quantification (UQ)) are displayed graphically for various biomarkers, although uncertainty can also be determined for measurements, models, costs, etc. This type of user interface allows a patient, physician, or another user to determine, for example, potential deviations or errors in measurements (e.g., due to differences in manufacturing assays, devices, technicians, and / or sample collection and viability, etc.) or risk due to modeling. In some cases, the user interface can allow the user to review and assess the uncertainty about the measurement, the uncertainty about the modeling, or the net uncertainty, defined as the aggregate of the uncertainty about the measurement and the modeling. In some embodiments, uncertainty can be used as a gauge to determine the confidence in a measurement in a quality control process. In some embodiments, uncertainty can be utilized in conjunction with user-provided data, metrics, and / or requirements to ensure that target outcomes (e.g., costs, likelihood of an event, etc.) fall within acceptable ranges. For example, this approach can be used to ensure that treatment costs do not exceed a certain limit or that the probability of a coronary heart disease (CHD) event remains below a defined threshold.

[0057] 12A and 12B are exemplary user interfaces that can be displayed by a display device to illustrate the quality control process in a method involving obtaining genetic, epigenetic, and / or other biomarker measurements (e.g., digital PCR (dPCR)) and / or modeling the resulting data. This type of user interface can demonstrate whether all sample measurements fall within expected reference measurements (FIG. 12A) or deviate from expected reference measurements (FIG. 12B) and are therefore considered test failures.

[0058] 13 is an exemplary user interface that may be displayed by a display device. In this example, the user interface displays a line graph showing changes in genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein (Y-axis) versus time (X-axis). This type of user interface may enable a patient, physician, or another user to determine changes (e.g., significant changes) over time for an individual patient, or for an individual patient compared to a particular specified population.

[0059] FIG. 14 is an exemplary user interface that can be displayed by a display device. In this example, the user interface displays a line graph showing change in score (Y-axis) against time (X-axis), e.g., based on one or more genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described above. In this example, the graph in the user interface shows threshold scores that distinguish patients between low and high scores, along with trends observed from population data. As described herein, the computer system 100 allows the user to operate and configure the display to present information in any number of different formats (e.g., displaying low and high score ranges for different patient populations). Changes in a patient's score over time can identify when a patient is or is not responding to a treatment strategy. Additionally, uncertainties (e.g., measurements, modeling, and / or aggregate measurements and modeling) can be used to further inform the user about the confidence in the risk group for the results. In some implementations, uncertainties may not be included.

[0060] 15 is an exemplary user interface that can be displayed by a display device. FIG. 15 shows a score based on, for example, genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein used to calculate the score. This type of user interface allows a user to evaluate the contribution of individual genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein to a score (e.g., risk probability, diagnostic probability, etc.).

[0061] Figure 16 is an exemplary user interface that may be displayed by a display device. The example user interface shown in Figure 16 illustrates the change in the contribution of a particular measurement and / or other data descriptive data (Y-axis) over time (X-axis) for a user or group of users.

[0062] 17 is an exemplary user interface that can be displayed by a display device. This example depicts a graphical representation of the contribution of genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein (e.g., SHAP scores, model weight, etc.) in a population (X-axis) for several different contributions of genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein (e.g., SHAP scores, model weight, etc.). As described herein, this user interface can be integrated, overlaid, superimposed, etc. with one or more user interfaces that display, for example, changes in the contribution of specific biomarkers over time.

[0063] Figure 18 is another exemplary user interface that can be displayed by a display device. Figure 18 shows a pie chart that can be used with information such as that contained in Table 4 below to perform univariate or multivariate analysis into various disease pathways in individuals and / or populations. This type of user interface can also be used to inform treatment and / or intervention recommendations.

[0064] (Table 4) TIFF2026508388000005.tif35128

[0065] 19A and 19B are exemplary user interfaces that can be displayed by a display device. In FIG. 19A, the computer system 100 generates a graph corresponding to an individual's treatment response (Y-axis) versus time (X-axis), based on, for example, genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described above. In FIG. 19B, the computer system 100 generates a graph corresponding to an individual's score (e.g., risk probability, diagnostic probability, etc.) (Y-axis) versus time (X-axis). Additionally, uncertainties (e.g., measurements, modeling, and / or aggregate measurements and modeling) can be used to further inform the user of the degree of confidence in the risk group for the results. In some implementations, uncertainties may not be included.

[0066] The example user interfaces shown in the previously referenced and described figures show data related to measurements of coronary heart disease. The underlying data used to generate such figures was measured by testing genetic and epigenetic markers for coronary heart disease (CHD). In some implementations, the underlying data can be obtained by testing, collating, querying, or aggregating appropriate genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data as described above for other diseases or disorders, such as stroke, heart failure, diabetes, etc. User interfaces can be generated based on such underlying data. In this manner, the online platform described herein can be used to present data related to not only cardiovascular disease but also other diseases and associated comorbidities. Genetic and epigenetic markers and numerous other indicators associated with CHD can be found, for example, in International Publication Nos. 2017 / 214397, 2022 / 051630 and 2022 / 051641, which are incorporated herein by reference.

[0067] As used herein, genetic biomarkers generally refer to single-nucleotide deletion polymorphisms (SNPs), but can refer to any type of genetic alteration (e.g., insertions, substitutions, etc.). As used herein, epigenetic biomarkers generally refer to the methylation of cytosine nucleotides, usually adjacent to guanine nucleotides (CpGs). As used herein, other biomarkers refer to proteins, metabolic assays, lipids, family history information, images, biochemistry, other data described herein, etc.

[0068] Computer Systems FIG. 20 is a block diagram of an example computer system that may be used to provide computational functionality associated with the algorithms, methods, functions, processes, flows, and procedures described in this disclosure, according to some implementations of the present disclosure. The illustrated computer is intended to encompass any computing device, such as a server, desktop computer, laptop / notebook computer, wireless data port, smartphone, personal digital assistant (PDA), tablet computing device, or one or more processors within these devices, including physical instances, virtual instances, or both. A computer may include input devices, such as a keypad, keyboard, and touchscreen, that can receive user information. A computer may also include output devices that can communicate information related to the operation of the computer. The information may include digital data, visual data, auditory information, or a combination of information. The information may be presented in a graphical user interface (UI) (or GUI).

[0069] The computer can act as a client, network component, server, database, persistence, or component of a computer system for performing the subject matter described in this disclosure. The illustrated computer is communicatively coupled to a network. In some implementations, one or more components of the computer can be configured to operate in different environments, including a cloud computing-based environment, a local environment, a global environment, and a combination of environments.

[0070] At the highest level, a computer is an electronic computing device operable to receive, transmit, process, store, and manage data and information related to the described subject matter. According to some implementations, a computer can also include or be communicatively coupled to an application server, email server, web server, caching server, streaming data server, or combination of servers.

[0071] A computer can receive requests over a network from a client application (e.g., running on another computer). The computer can respond to the received request by processing the received request using a software application. Requests can also be sent to the computer from internal users (e.g., from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers.

[0072] Each of the components of a computer can communicate using a system bus. In some implementations, any or all of the components of a computer, including hardware or software components, can interface with each other or with an interface (or a combination of both) over the system bus. The interface can use an application programming interface (API), a service layer, or a combination of an API and a service layer. An API can include specifications for routines, data structures, and object classes. An API can be computer language independent or dependent. An API can refer to a complete interface, a single function, or a set of APIs.

[0073] The service layer can provide software services to the computer and other components (shown or not shown) communicatively coupled to the computer. The functionality of the computer can be accessible to all service consumers using the service layer. Software services, such as those provided by the service layer, can provide reusable defined functionality via defined interfaces. For example, the interface can be software written in JAVA, C++, or a language that provides data in Extensible Markup Language (XML) format. Although illustrated as an integrated component of the computer, in alternative implementations, the API or service layer can be a standalone component relative to other components of the computer and other components communicatively coupled to the computer. Moreover, any or all portions of the API or service layer can be implemented as a child module or sub-module of another software module, an enterprise application, or a hardware module without departing from the scope of this disclosure.

[0074] The computer may include an interface. While a single interface is illustrated in FIG. 20, two or more interfaces may be used depending on the particular need, desire, or specific implementation of the computer and the described functionality. The interface may be used by the computer to communicate with other systems (shown or not) connected to a network in a distributed environment. Generally, the interface may include or be implemented using coded logic in software or hardware (or a combination of software and hardware) operable to communicate with the network. More specifically, the interface may include software supporting one or more communication protocols associated with the communication. In this manner, the network or interface hardware may be operable to communicate with physical signals internal and external to the illustrated computer. Any of the user interfaces and / or other interfaces described herein based on genetic biomarkers, epigenetic biomarkers, other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein may be displayed, tracked, and shared via the computer with application environments (e.g., mobile application environments, web application environments, etc.), coaching, and / or connected devices.

[0075] A computer may include a processor. Although shown as a single processor in Figure 20, two or more processors may be used depending on the particular need, desire, or specific implementation of the computer and its described functionality. Generally, a processor may execute instructions and manipulate data to perform the operations of a computer, including operations using the algorithms, methods, functions, processes, flows, and procedures described in this disclosure.

[0076] The computer may also include a database capable of maintaining data for the computer and other components connected via a network (shown or not shown). For example, the database may be an in-memory, conventional, or database storing data consistent with this disclosure. In some implementations, the database may be a combination of two or more different database types (e.g., a hybrid of an in-memory database and a conventional database) depending on the particular need, desire, or specific implementation and described functionality of the computer. Although shown as a single database in FIG. 20, two or more databases (of the same type, different types, or a combination of types) may be used depending on the particular need, desire, or specific implementation and described functionality of the computer. While the database is shown as an internal component of the computer, in alternative implementations, the database may be external to the computer.

[0077] The computer may also include memory capable of retaining data for the combination of components connected to the computer or network (shown or not shown). The memory may store any data consistent with this disclosure. In some implementations, the memory may be a combination of two or more different types of memory (e.g., a combination of semiconductor and magnetic storage) according to a particular need, desire, or specific implementation and described functionality of the computer. Although shown as a single memory in FIG. 20, two or more memories (of the same type, different types, or a combination of types) may be used according to a particular need, desire, or specific implementation and described functionality of the computer. While the memory is shown as an internal component of the computer, in alternative implementations, the memory may be external to the computer.

[0078] An application can be an algorithmic software engine that provides functionality according to a particular need, desire, or specific implementation of a computer and the described functionality. For example, an application can serve as one or more components, modules, or applications. Furthermore, while illustrated as a single application, an application can be implemented on a computer as multiple applications. Additionally, while illustrated as internal to a computer, in alternative implementations, an application can be external to the computer.

[0079] The computer may also include a power supply. The power supply may include a rechargeable or non-rechargeable battery, which may be configured to be either user-replaceable or non-user-replaceable. In some implementations, the power supply may include power conversion and management circuitry, including recharge, standby, and power management functions. In some implementations, the power supply may include a power plug that allows the computer to be plugged into a wall socket or power source, for example, to power the computer or to recharge a rechargeable battery.

[0080] There can be any number of computers associated with or external to the computer system including the computer, each computer communicating over a network. Furthermore, the terms "client," "user," and other suitable terms may be used interchangeably where appropriate without departing from the scope of this disclosure. Moreover, this disclosure contemplates that many users may use one computer, and that one user may use multiple computers.

[0081] To achieve the described performance characteristics, the model for processing the described inputs and returning the described outputs can include a classification model structured to receive input data and return an indication of a subset of features that have high predictive power (e.g., sensitivity, specificity, cost, etc.).

[0082] Different types of models (e.g., linear, nonlinear, classification, etc.) can be built using one or more markers for various purposes (e.g., cost, diagnostics, etc.). Multiple models can be built using one or more markers for various purposes (e.g., cost, diagnostics, etc.).

[0083] Model architectures can include statistical (e.g., linear regression, logistic regression, proportional hazards models, etc.), machine learning (e.g., random forests, support vector machines, neural networks, Bayesian classifiers, etc.), deep learning (e.g., convolutional neural networks, recurrent neural networks, autoencoders, large-scale language models, etc.), and time series (e.g., ARIMA, etc.), Bayesian models (e.g., Bayesian networks, etc.), and financial (decision trees, discrete event simulation, financial impact, etc.).

[0084] Before fitting to the model, the input data can be conditioned or otherwise preprocessed so that the conditional data elements (e.g., genomic reads associated with the locus of interest, functional data, sensor data, other lifestyle data, etc.) are suitable for further processing. Conditioning, as described herein, can include filtering data (e.g., sensor data outputs with confidence values ​​below a threshold, etc.). Before training the model, the model input can undergo a preprocessing step such as dimensionality reduction (e.g., principal component analysis, linear discriminant analysis, autoencoder, uniform manifold approximation and projection (UMAP), partial least squares regression, etc.).

[0085] Transforming the output data can be used to enhance the scalability of the model. For example, SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostics Explanations, Integrated Gradients, Partial Dependence Plots, Global Surrogate Models, etc. can be used to enhance the scalability of the output for the user. One or more of these approaches can be used simultaneously. In a specific example, SHAP can be used to identify markers that most significantly contribute to a condition or symptom.

[0086] Transforming the output data into digital objects such as visualizations to facilitate the generation of characterizations, interventions for condition prevention, interventions for condition treatment, and / or other actions (e.g., computer-generated and machine-implementable instructions) for improving and / or maintaining metrics (diagnoses, risk scores, costs, etc.). In particular examples, when transforming input data, various software (e.g., Rstats, ggplotz, matplotlib, etc.) can be used in sequence and / or in parallel to generate the digital objects and visualizations.

[0087] The uncertainty of a model prediction (e.g., risk score, diagnosis, etc.) can be calculated. Methods for estimation can include bootstrap methods, Bayesian methods, Monte Carlo dropout, ensemble methods, sensitivity analysis, etc. The uncertainties of the methods and / or models described herein can be aggregated to provide an overall system uncertainty.

[0088] In some variations, the methods described herein may further include refining the model by collecting a set of training data streams derived from a patient population, the set of training data streams capturing data (e.g., genetic, epigenetic, lifestyle, etc.) paired with intervention, treatment, and management information from the patient population, generating a training dataset derived from the set of training data streams and the set of transformation operations, and training the model in one or more stages based on the training dataset.

[0089] Data / signal inputs and / or other inputs (e.g., contextual inputs, derived inputs, combined inputs, etc.) can be used to train the model. More specifically, markers can be transformed individually or in combination before being processed by the model. Combined markers can include genetics, epigenetics, metabolic assays, lipids, proteins, lifestyle habits, demographics, and / or other data described herein.

[0090] Additionally or alternatively, dynamic aspects of the features derived from the samples (e.g., changes in markers over time, changes in frequency between instances of each feature, other temporal aspects, other frequency-related aspects, etc.) can be used to predict or otherwise anticipate health status for generation of individualized intervention plan components.

[0091] Inputs may be aggregated from patient populations associated with different demographic characteristics, health status, health conditions, lifestyle habits, and / or other suitable factors.

[0092] With respect to model architecture, inputs to the models described herein can produce outputs that are subsequently used as inputs to an overarching model (e.g., a classification model with multiple layers, a reinforcement learning model, etc.) that returns aspects of a diagnosis, patient characterization (e.g., with respect to health status, medical condition, etc.), individualized intervention plan, and / or other aspects based on processing the data step-by-step. However, the models can implement other suitable architectures having other suitable flows for processing data derived from the inputs.

[0093] The returned classification regression and / or other output of the model can include a confidence-related parameter returned in such classification. In particular, the confidence-related parameter can have a score (e.g., a percentile, other score) indicating the confidence in the returned output. The confidence can be estimated by aggregating measurements and / or modeling uncertainty.

[0094] Additionally, refined versions of the model can be configured to process fewer inputs (e.g., only a subset of the inputs described herein) to return accurate outputs for generating individualized intervention plan components. Additionally, previous data derived from the inputs (e.g., new signals / signatures, signals / signatures of interest, etc.) can be returned by computing the components during model refinement.

[0095] Although aspects, variations, and examples of models (e.g., with respect to inputs, outputs, and training) are described herein, the models may additionally or alternatively include other machine learning architectures.

[0096] The statistical analysis and / or machine learning algorithms may be characterized by a learning style including any one or more of supervised learning (e.g., using a backpropagation neural network), unsupervised learning (e.g., K-means clustering), semi-supervised learning, reinforcement learning (e.g., using a Q-learning algorithm, using temporal difference learning, etc.), and any other suitable learning style.

[0097] Furthermore, any algorithm may implement any one or more of a regression algorithm, an instance-based method (e.g., k-nearest neighbors, learning vector quantization, self-organizing maps, etc.), a regularization method, a decision tree learning method (e.g., classification and regression trees, chi-squared approaches, random forest approaches, multivariate adaptive approaches, gradient boosting machine approaches, etc.), a Bayesian method (e.g., naive Bayes, Bayesian belief networks, etc.), a kernel method (e.g., support vector machines, linear discriminant analysis, etc.), a clustering method (e.g., k-means clustering), an association rule learning algorithm (e.g., an Apriori algorithm), an artificial neural network model (e.g., backpropagation, Hopfield networks, learning vector quantization, etc.), a deep learning algorithm (e.g., Boltzmann machines, convolutional networks, stacked autoencoders, etc.), a dimensionality reduction method (e.g., principal component analysis, partial least squares regression, etc.), an ensemble method (e.g., boosting, bootstrap aggregation, gradient boosting machine approaches, etc.), and any suitable type of algorithm.

[0098] In an example, the training of the model can be based on a cohort of patients who have lost >5% of their body weight while taking medication (e.g., statins) and / or receiving personalized digital care for weight loss, and who exhibit a concomitant reduction in cardiovascular symptomatology and / or risk. Diagnostic signatures can be constructed based on predictive variables identified in the model regarding the likelihood that a patient will have a cardiovascular event. Example variables include demographics, genomics (e.g., SNPs and / or methylation associated with inflammation, obesity, other factors described, etc.), and any other type of variable.

[0099] Intervention, treatment, management, and / or monitoring can also include modified variations of operations for training and refinement of other model architectures configured to improve other conditions.

[0100] A system / platform for intervention, diagnosis, treatment, management, and / or monitoring includes a computing platform including one or more processing subsystems including a non-transitory computer-readable medium having instructions stored thereon that, when executed by the processing subsystem, perform one or more steps of the methods described herein; and one or more execution subsystems configured to execute components of an individualized intervention plan informed by the processes of the computing platform. In a variant, the execution subsystem can be configured to execute control instructions generated by the computing platform, where the control instructions can include instructions for controlling the operation of one or more of: an application interface (e.g., a mobile application interface, a web application interface, etc.) for signal reception, data aggregation, and / or retrieval of other inputs to be processed by the system architecture; a sample processing architecture (e.g., by an automated / robotic subsystem); a communication interface (e.g., by a human entity, by a digital entity) for performing telehealth operations, providing group therapy, or providing counseling; an interface for providing rewards and / or other incentives to a patient; an interface for providing tasks to a patient and monitoring the provided tasks; an interface for connecting a patient's device (e.g., a biometric monitoring device) with the patient's account in the system / platform; an interface for providing medication therapy to a patient; an interface for providing dietary advice / tracking the patient's eating behavior; and / or other suitable functions for delivering components of an individualized intervention plan.

[0101] Although embodiments of the system are configured to perform one or more portions of the methods described herein; variations of the system can be configured to perform other suitable methods.

[0102] The computing platform may include one or more processing subsystems including a non-transitory computer-readable medium having instructions stored thereon that, when executed by the processing subsystem, perform one or more steps of the methods described herein; and one or more execution subsystems configured to execute components of an individualized intervention, treatment, management, and / or monitoring plan informed by the processes of the computing platform.

[0103] The execution subsystem can be configured to execute control instructions generated by the computing platform, where the control instructions may include instructions for controlling the operation of one or more of: an application interface (e.g., a mobile application interface, a web application interface, etc.) for signal reception, data aggregation, and / or retrieval of other inputs to be processed by the system architecture; a sample processing architecture (e.g., by an automated / robotic subsystem), a communication interface (e.g., by a human entity, by a digital entity) for performing telehealth operations, providing group treatment, or providing counseling; an interface for providing rewards and / or other incentives to a patient; an interface for providing tasks to a patient and monitoring the provided tasks; an interface for connecting a patient's device (e.g., a biometric monitoring device) with the patient's account in the system / platform; an interface for providing medication therapy to a patient; an interface for providing patient recommendations / tracking lifestyle / behavior or other measurements or biomarkers; and / or other suitable functions for delivering components of a personalized intervention, treatment, management and monitoring plan.

[0104] In particular, the execution subsystem can be structured to implement next-generation prescription-grade digital therapeutics or other programs that use artificial intelligence (AI) to analyze genetic biomarkers, epigenetic biomarkers, and / or other biomarkers (e.g., proteins, metabolic assays, etc.), measurements, test results, and / or other data described herein to generate evidence-based personalized care, intervention, treatment, management, and monitoring programs to improve targeted outcomes (e.g., risk reduction, cost reduction, etc.).

[0105] Uses and Benefits The compositions and methods described herein provide systems and methods for generating, collating, aggregating, evaluating, analyzing, displaying, tracking, and sharing standalone or integrated (genomic, epigenomic, proteomic, RNA, imaging, electrocardiogram, lifestyle factors, clinical markers, social determinants of health, etc.) diagnoses, risk assessments, treatment pathways, cost estimates, prognoses, and health outcomes for care (e.g., for prevention, diagnosis, treatment, and / or management), or for understanding, managing, and mitigating risks for various conditions or symptoms (e.g., for implementing cost reduction initiatives). In examples, characterization can be used to provide actionable insights with interventions, including lifestyle changes and / or therapies (e.g., drug therapy, medical device, dietary therapy, stem cell therapy) and / or interventions (e.g., gene editing, base editing, epigenetic silencing, epigenetic editing). Additionally or alternatively, data from patients, its analysis, and interpretation may also elucidate more complex diagnostic pathways and pinpoint potential treatment and / or intervention options. Additionally or alternatively, the systems and methods can be used to continuously monitor and evaluate the effectiveness of such treatments and / or interventions, providing data and actionable insights for further optimization of conditions, symptoms, health outcomes or costs.

[0106] In some embodiments, the compositions and methods described herein can be used for risk assessment, prevention, prognosis, management, intervention, treatment, and / or monitoring of one or more diseases, disorders, or risks. In some embodiments, the compositions and methods described herein can be used for risk assessment, prevention, prognosis, management, intervention, treatment, and / or monitoring of symptoms at more than one time point.

[0107] The compositions and methods described herein can be used to recommend lifestyle changes and / or therapies and / or interventions and / or management strategies to optimize a targeted outcome (e.g., reducing costs, reducing the likelihood of a heart attack). The compositions and methods described herein can also be used to monitor or optimize the effectiveness of lifestyle changes and / or therapies and / or interventions and / or management strategies over time to optimize a targeted outcome (e.g., reducing costs, reducing the likelihood of a heart attack).

[0108] The compositions and methods described herein can also estimate the benefits of new interventions, such as new pharmaceutical and / or other compositions, including, but not limited to, chemical compositions (e.g., small molecule modulators), biological compositions (e.g., pre- / pro- / syn- / post-biotics), gene editing, base editing, epigenetic silencing, and epigenetic editing. Additionally, the compositions and methods described herein can be used to apply the output of the analysis to take one or more actions (e.g., recommend new health initiatives) to achieve an intended outcome (e.g., address obesity in employees).

[0109] The compositions and methods described herein encompass a comprehensive system designed to manage marker data (e.g., genomic biomarkers, epigenomic biomarkers, and / or other biomarkers) and other data or measurements derived from processed samples. The system also includes mechanisms for deriving insights by analyzing these biomarkers using models (e.g., linear regression, machine learning) and for facilitating personalized care / intervention / management / treatment strategies. As described herein, these processes can be integrated and data and insights can be delivered to patients or other users in a customized manner via computer system 100 and display devices.

[0110] The compositions and methods described herein can be used to query, collate, aggregate, evaluate, and analyze information from one or more of financial (e.g., cost of procedures, cost of medication, claims, etc.), industry / prevalence (e.g., prevalence of heart disease in a particular geographic area, number of heart attacks per year among truck drivers, etc.), publications (e.g., journal articles, clinical guidelines, etc.), clinical testing (e.g., epigenetic assessment, inflammation biomarkers, etc.), imaging (e.g., FFR from CCTA, etc.), medical (e.g., ICD10 codes, electronic health records, medication use, etc.), clinical (e.g., blood pressure, weight, etc.), treatment (e.g., cost of statins, drugs that reduce inflammation, etc.), lifestyle (e.g., smoking, frequency of exercise, etc.), supplement use (e.g., vitamin D, vitamin B12, iron, etc.), and data to display, track, and derive insights about the user in a customized manner.

[0111] The methods can be used to generate, collate, aggregate, evaluate, analyze, display, track, and share standalone or integrated (genomic, epigenomic, proteomic, RNA, imaging, electrocardiogram, lifestyle factors, clinical markers, social determinants of health, etc.) diagnoses, risk assessments, treatment pathways, cost estimates, prognoses, health outcomes for care (e.g., for prevention, diagnosis, treatment, and / or management), or to understand, manage, and mitigate risks for various conditions or indications (e.g., for implementing cost reduction initiatives). Consequently, these methods have the potential to enhance outcomes for one or more health states or indications, resulting in precision medicine, personalized risk assessment, prevention, management, treatment, and monitoring, leading to refined approaches.

[0112] The received or generated measurements can be data or information (e.g., datasets) including, but not limited to, clinical diagnoses; demographics (e.g., gender, race); lifestyle habits; costs; claims (e.g., health insurance); imaging (e.g., cardiac CT scan, cardiac MRI, coronary angiogram); features derived from imaging (e.g., FFR, percent stenosis from a CT scan); results from electrocardiogram or echocardiogram tests; results from stress tests; blood tests (e.g., for metabolic assays, genes, epigenetics, proteins, etc.); blood pressure; results from carotid ultrasound tests, etc. The received or generated data or information can also include publications (e.g., journal articles) and, for example, clinical guidelines.

[0113] One or more datasets can be received from a patient or patient stakeholder (e.g., a clinician), which further functions to enable generation of a baseline or subsequent state and one or more measurements from which a model can be generated in a subsequent portion of the method to return a diagnosis, risk assessment, personalized treatment pathway, cost estimate, prognosis, health outcome, risk, other insight of interest, or recommendation.

[0114] The dataset can include data from one or more of weight (e.g., receiving a patient's weight value generated from a digital scale), body fat percentage, muscle mass, body water, height or other length measurements (e.g., with a ruler or tape measure), other body mass index (BMI)-related parameters, blood chemistry and biochemistry information, inflammatory markers, fasting blood glucose, high-density lipids, low-density lipids, blood interleukins, c-reactive protein, blood counts, electrophysiology signals (e.g., electroencephalogram signals, electromyogram signals, galvanic skin response signals, electrocardiogram signals, etc.), heart rate, body temperature, cardiovascular parameters, continuous glucose monitoring (glycemic response), respiratory parameters (e.g., respiration rate, deep / shallow breathing, etc.), blood oxygen signals, exercise parameters, and any other suitable physiologically related parameters of the patient. Additionally and alternatively, the dataset can include data from one or more of electronic health records, health plan claims, questionnaires, surveys, wearables, publicly available sources (e.g., repositories), and any other data directly or indirectly related to the patient or user. Additionally and alternatively, the dataset may include data that is raw, imputed, transformed, longitudinal, cross-sectional, or temporal.

[0115] The dataset may capture behavioral information about the patient related to one or more of energy level (e.g., morning energy level, evening energy level, daytime energy level, etc.), eating behavior, sleep behavior, stress level, stress-related events, cravings, exercise behavior, meditation behavior, perceived progress toward health-related goals, actual progress toward health-related goals, symptom status, social determinants of health, familial determinants of health, and occupational determinants of health, and / or other behavioral information. In an example, the lifestyle dataset may capture one or more of the following lifestyle characteristics of the patient: energy level, food intake (e.g., via food photos, which are then determined and scored by a coaching entity or other entity, via food diary entries, via a food monitoring application's application programming interface (API), etc.), sleep behavior, stress level, cravings, exercise behavior, and weight loss progress. However, variations of the example may alternatively capture other types of lifestyle information from the patient.

[0116] The dataset can include data from one or more of blood chemistry and biochemistry profiles, saliva chemistry and biochemistry profiles, medication use, supplement use, fasting blood glucose, blood glucose response, high density lipid values, low density lipid values, and any other similar parameters. The dataset can capture one or more of morning energy level, eating behavior, sleep behavior, stress level, appetite, exercise behavior, meditation behavior, patient symptom status, medication use, social determinants of health, familial determinants of health, and occupational determinants of health.

[0117] The dataset can capture context and additional information at one or more levels of population, location or geography, employer, health plan, government entity, nonprofit organization, medical institution, and any other similar parameters. Additionally or alternatively, the dataset can include or otherwise detect circulating biometric indicators or biometric indicators that appear in a pattern (e.g., derived from user input, a biometric monitoring device worn by the user, or determined by an algorithm that tracks the user's physical, emotional, or neurological state). The dataset can include data sampled from a device once (e.g., at a single time point, upon patient ingestion) or at several time points (e.g., at random time points, periodic time points, in connection with an event trigger, at other frequencies, etc.).

[0118] Other supplemental or contextual data relevant to generating actionable insights can be received. For example, the method can include capturing the patient's current treatment approach (e.g., existing medications, existing supplements, etc.), trends in use or adherence to the patient's current treatment approach (e.g., increasing use, decreasing use, steady use, etc.), medical history, family medical history, and / or other information. Additionally or alternatively, the additional data can include information related to the user's environment, such as the user's location (e.g., as determined from a global positioning device, triangulation device), the user's environmental temperature, the user's environmental sounds, and any other suitable environmental information related to potential stimuli affecting health, and / or environmental devices that can be used for output related to subsequent blocks of the method (e.g., diagnosis, treatment, etc.). Additionally or alternatively, the additional data can include information from decisions or procedures performed by the user, such as imaging (e.g., information from an angiogram, coronary calcium scan, CCTA, etc.), interventions (e.g., gene editing, epigenetic silencing, etc.), and any other similar technologies or methodologies.

[0119] The compositions and methods described herein can be adapted for distinct populations (e.g., Midwestern Americans, Native Americans, etc.), various demographics (e.g., age, gender, etc.), different health conditions (e.g., diabetes, known risk factors, etc.), different lifestyles (e.g., smoking, type of diet, etc.), different users (e.g., clinicians, employers, insurance companies, etc.), different interventions (e.g., drug A vs. drug B), and / or other factors (e.g., social determinants of health, family medical history, etc.). These adaptations are aimed at promoting health outcomes, improving care, and / or understanding, managing, and mitigating risk.

[0120] Individualized intervention methods After receiving measurements for a patient, the compositions and methods described herein can include (A) quantifying the uncertainty of the marker; (B) performing quality control of the sample and / or marker; (C) performing a risk assessment and / or diagnosis; (D) quantifying the uncertainty to assess the confidence of the determination; (E) determining the contribution of one or more biomarkers; (F) identifying contingencies or associations between the biomarkers and disease and treatment pathways; (G) generating personalized interventions, treatments, management, and / or monitoring for the patient or user; (H) predicting estimated outcomes and / or costs associated with the personalized interventions, treatments, management, and / or monitoring; and (I) and / or implementing the personalized interventions, treatments, management, and / or monitoring for the patient. See Table 5.

[0121] (Table 5) TIFF2026508388000006.tif60145

[0122] The order of operations may vary, and some steps may be unnecessary for the system to operate. This process may be repeated multiple times to monitor and determine the effectiveness of intervention, treatment, management, and / or monitoring, and to update intervention, treatment, management, and / or monitoring recommendations. The following description of Table 5 is merely exemplary and is not intended to be limiting.

[0123] Column (A) relates to quantification of uncertainty, which can encompass all aspects of the marker measurement. For example, in epigenetic measurements, quantification of uncertainty can include, but is not limited to, sampling error, reagent quality, instrumentation, and human error.

[0124] Column (B) relates to the quantification of uncertainties referred to herein, which can be used for quality control. For example, a known reference sample and / or measurement can be compared to a new measurement. The difference between the known and new measurements can be used to assess their uncertainties, as well as the error and / or uncertainty and how they compare to predetermined acceptable thresholds. This process helps identify sources of error and / or uncertainty and minimizes such error and / or uncertainty to ensure confidence in the measurements. For example, this can be used to determine whether a sample requires remeasurement or recollection to meet specified acceptable thresholds for measurements and / or markers.

[0125] Column (C) relates to, upon processing the data (e.g., imaging, lifestyle, etc.) and / or measurements (e.g., DNA methylation, genetic biomarkers, etc.), returning a baseline or subsequent state and a set of signatures (e.g., diagnostic and / or treatment signatures associated with the patient's genomic and demographic characteristics). Column (C) involves converting the data into various signatures that can be used to characterize the patient and to generate a personalized intervention, treatment, management and / or monitoring plan for the patient based on the patient's characterization.

[0126] The transformation operations can include one or more of graph-based methods, linear and nonlinear dimensionality reduction, application of supervised, semi-supervised, and unsupervised machine or statistical inference methods to derive informative features from measurements and / or data, imputing missing regions, determining the genetic ancestry of a patient, estimating genetic parameters including gene variation and homozygosity, estimating scores representing inherited risk, estimating scores representing acquired risk, and estimates of qualitative, quantitative, continuous, and categorical traits normalized to biological sex, genetic ancestry, age, socioeconomic status, measurements, lifestyle variables, and any other appropriate parameters. In some cases, traits can be normalized to biological sex, genetic ancestry, age, socioeconomic status, measurements, lifestyle variables, and any other appropriate parameters.

[0127] For measurements and / or monitoring devices described herein, column (C) may also include signal processing operations, including one or more of noise removal, filtering, smoothing, clipping, converting discrete data points to a continuous function, and any other suitable signal conditioning process. For example, some variations of column (C) may additionally include performing a windowing operation and / or a signal cleaning operation. Signal cleaning may include removing signal anomalies through one or more filtering techniques. In particular examples, filtering may include one or more of Kalman filtering, bootstrap filtering, particle filtering, Markov chain Monte Carlo filtering, and / or another suitable technique. Signal cleaning may thus improve the quality of the data for further processing with respect to one or more of noise, sensor equilibration, sensor drift, environmental effects (e.g., humidity, physical disturbances, etc.), and any other suitable types of signal artifacts.

[0128] The baseline status serves to establish a reference state against which progress will be compared as the patient participates in the generated personalized intervention, treatment, management, and / or monitoring plan. The baseline status preferably relates to the patient's condition prior to participation in the personalized intervention, treatment, management, and / or monitoring plan (e.g., epigenetic baseline status, coronary heart disease baseline status, etc.), thereby characterizing the patient's condition prior to treatment or intervention or care according to the personalized intervention, treatment, management, and / or monitoring plan. However, the baseline status may alternatively characterize another appropriate condition of the patient. Additionally, the methods described herein may include re-establishing the patient's "baseline" status in conjunction with the provision of the personalized intervention, treatment, management, and / or monitoring plan. In this manner, the baseline status can be updated as the patient progresses while participating in the personalized intervention, treatment, management, and / or monitoring plan, at which point data, prognosis, outcomes, costs, risks, diagnostic and / or treatment signatures may guide the patient toward a different personalized intervention, treatment, management, and / or monitoring plan.

[0129] The baseline state can also characterize the patient's physiological state with respect to a condition or indication (e.g., a health condition, a disease state, etc.). As such, the baseline state can characterize a clinical diagnosis, a test diagnosis, a radiological diagnosis, a tissue diagnosis, a principal diagnosis, an admission diagnosis, a differential diagnosis, a prenatal diagnosis, a diagnosis of exclusion, and / or other diagnostic criteria. Additionally or alternatively, the baseline state can characterize an aspect related to a condition or indication (e.g., high cholesterol, etc.). In some embodiments, the baseline state can characterize a risk assessment, a treatment pathway, a cost estimate, a prognosis, a health outcome, or any other suitable parameter.

[0130] In some embodiments, the diagnostic signature generated in column (C) can include one or more of measurements (e.g., genetic signature, epigenetic signature, epigenetic signature associated with coronary heart disease, HbA1c, etc.), additional data indicative of a decreased or increased likelihood of developing a health condition or symptom (e.g., lifestyle, demographics, imaging, etc.). In some embodiments, a signature based on one or more of the factors and data outlined herein can be for risk assessment, treatment pathways, cost estimation, prognosis, health outcome, or any other suitable parameter.

[0131] In some embodiments, the diagnostic signature generated in column (C) can include one or more of measurements (e.g., genetic signatures, epigenetic signatures, coronary heart disease-related epigenetic signatures, HbA1c, etc.), additional data (e.g., lifestyle, demographics, imaging, etc.) indicative of a reduced / increased likelihood of improvement or remission of a health condition or symptom. The signature can be used to develop lifestyle change recommendations and / or treatments (e.g., medications, medical devices, dietary therapies, stem cell therapies) and / or interventions (e.g., gene editing, base editing, epigenetic silencing, epigenetic editing) and / or care, and / or to understand, manage, and / or mitigate risk. Additionally or alternatively, the interventions can be used to generate personalized intervention, treatment, management, and / or monitoring plans for individualized and combined treatment pathways, care, interventions, and risk management implementing different approaches.

[0132] The biomarker signatures and baseline or subsequent conditions, as well as the diagnoses, risk assessments, personalized therapies, cost estimates, prognoses, and health outcomes generated using the compositions and methods described herein can target one or more health conditions or indications.

[0133] Column (G) indicates that processing the baseline or subsequent state, the set of diagnostic signatures, and the set of treatment and intervention signatures through the model may include a personalized intervention, treatment, management, and / or monitoring plan for the patient, with the model functioning to convert the signatures into a customized combination of treatments and / or interventions and / or management and / or care approaches specialized to improve, or maintain and / or ameliorate, the patient's health status or symptoms. Instead of a diagnostic signature, the signature can be a signature of a risk assessment, a treatment pathway, a cost estimate, a prognosis, a health outcome, or any other suitable parameter.

[0134] The one or more markers and / or the one or more models can also be used to provide a patient and / or user with personalized intervention, treatment, management, and / or monitoring. The one or more markers and / or the one or more models can also be used as a basis for designing and / or developing and / or optimizing personalized intervention, treatment, management, and / or monitoring for a patient. For example, the one or more markers and / or the one or more models can also be used for drug discovery, drug development, understanding drug efficacy in a research or clinical or clinical trial setting, understanding or predicting side effect profiles, defining eligibility criteria for research or clinical trials.

[0135] The one or more markers can be used to measure the impact (e.g., cost reduction, improved health status, etc.) for personalized intervention, treatment, management, and / or monitoring for the patient and / or user. The impact of the one or more markers can be quantified for personalized intervention, treatment, management, and / or monitoring for the patient and / or user. The impact of the one or more markers can be re-evaluated over time by continuous monitoring (measuring and collecting data) for personalized intervention, treatment, management, and / or monitoring for the patient and / or user. The re-evaluated impact of the one or more markers can be used to optimize personalized intervention, treatment, management, and / or monitoring for the patient and / or user. For example, re-evaluation of the one or more markers can also be used to optimize drug compounds and / or dosages, etc. The re-evaluated impact of the one or more markers can be used to optimize personalized intervention, treatment, management, and / or monitoring for the patient and / or user. Re-evaluation of the one or more markers and / or one or more models can occur at regular or random time intervals.

[0136] A model is preferably constructed to process the signatures and / or baseline conditions and / or subsequent conditions generated from column (C) and to return a personalized intervention, treatment, management and / or monitoring plan specific to the patient and / or user. The model can return a set of measurements (e.g., SNPs, methylation markers, etc.) and insights (e.g., percentage of employees at high risk of heart attack, etc.) for the patient and / or user and generate those personalized intervention, treatment, management and / or monitoring plans from the measurements, data and / or insights.

[0137] The personalized intervention, treatment, management, and / or monitoring plan may have subportions (e.g., modules, phases) that may include one or more of a personalized drug therapy regimen, a personalized supplement regimen, personalized lifestyle recommendations, medical procedures, testing recommendations, preventative health care treatment approaches, and / or other appropriate aspects. Furthermore, the personalized intervention, treatment, management, and / or monitoring plan may be characterized by a duration to allow the patient to complete the program and achieve one or more health goals. Thus, generating the personalized intervention, treatment, management, and / or monitoring plan may include returning in-person and / or digitally delivered recommendations (e.g., drug recommendations, gene editing, epigenetic silencing, target BMI, etc.) and / or care components, coaching components, and risk management components based on the patient's measurements, data, and / or insights. However, personalized intervention, treatment, management, and / or monitoring plans may be configured in other ways.

[0138] Column (G) can include generating instructions that can be executed by a system having a computational element. For example, one or more components of a personalized intervention, treatment, management, and / or monitoring plan can be digitally delivered via a mobile device application with an application interface between a care or coaching entity and the patient being treated.

[0139] In one example, the individualized intervention, treatment, management and / or monitoring plan can have a set duration (e.g., 36 weeks, 24 weeks, 12 weeks, another suitable number of weeks, etc.) and can be configured for delivery with the patient using one or more interfaces (e.g., a web interface, a mobile device interface, a wearable computing device interface, a telephone interface, an interpersonal interface, an interface with one or more robotic devices, etc.).

[0140] An example personalized intervention, treatment, management, and / or monitoring plan can have one or more phases configured to promote changes in a patient's marker or aggregation of markers, provide a therapeutic intervention, and achieve a desired result in terms of the generated diagnosis and characterization (e.g., based on a diagnostic signature processed by a model, based on a therapeutic signature processed by a model, etc.). In some variations, the desired result can also relate to a risk assessment, a treatment pathway, a cost estimate, a prognosis, a health outcome, or any other suitable parameter.

[0141] In one example, the first phase has a duration (e.g., 4 weeks, or another appropriate number of weeks) configured to guide the patient to focus on baseline development of health status, symptoms, insights, and / or habits with functionality for tracking lifestyle habits via a mobile device application. In an example, the second phase has a duration (e.g., 12 weeks, or another appropriate number of weeks) focused on individualized therapy, care, and management approaches to create behavioral change for the patient. In an example, the third phase has a duration (e.g., 8 weeks, or another appropriate number of weeks) focused on individualized therapy, care, and management approaches to stabilize the patient's life in relation to maintaining a desired state (e.g., building and maintaining healthy habits, preventing relapse, maintaining remission, etc.).

[0142] With respect to a personalized intervention, treatment, management, and / or monitoring plan, the platform can provide kits (e.g., genetic and epigenetic sampling kits, connected devices, biometric devices, instructions, etc.) whose interaction by the patient facilitates the establishment of a physiological baseline that can serve as a reference point for moving forward with respect to the personalized intervention, treatment, management, and / or monitoring plan. In an example, the kit can be mailed to the patient's home or location (e.g., if facilitated by the platform). The kit also provides functionality for receiving inputs related to the lifestyle datasets described herein, as well as instructions for downloading an application to the platform using an interface. In one specific example, the patient is prompted to download a mobile application, complete a dietary lifestyle form, and schedule a counseling session as part of the personalized therapy, care, and management approach.

[0143] Subsequently, with regard to the personalized intervention, treatment, management, and / or monitoring plan, the platform can guide and assist the patient using counseling entities (e.g., professional counselors, clinicians, artificial intelligence counseling entities, etc.) over a period of time (e.g., weeks, months) with regular sessions (e.g., weekly and biweekly sessions) that can be scheduled and / or ad hoc. A particular example counseling session can be 15-20 telephone behavioral counseling sessions, although example variations can have a different duration and / or be provided in a different format. The personalized intervention, treatment, management, and / or monitoring plan provides personalized therapy and intervention pathways configured to restructure the patient's condition or symptoms to achieve a goal (e.g., a 5-10% reduction in risk score). With respect to the individualized intervention, treatment, management, and / or monitoring plan created in column (G), individualized, actionable insights are returned by the model and delivered to the patient (e.g., via an entity, application interface, other interface with the platform, etc.) as the patient completes phases of the individualized intervention, treatment, management, and / or monitoring plan (e.g., completes tasks, interacts with modules, interacts with tools to monitor diet (e.g., by uploading photo documentation of meals / diet / intake), monitors the patient's lifestyle vitals (e.g., progress on smoking cessation, exercise, weight loss, etc., via mobile application and wireless scale, etc.). Patients can engage with the individualized intervention, treatment, management, and / or monitoring plan at any time or place via their mobile device application and / or web application, allowing for flexible participation. Additionally or alternatively, the individualized intervention, treatment, management, and / or monitoring plan can provide time-sensitive tasks and / or prompt timely interactions regarding the patient's more acute condition (e.g., triggering event, such as a heart attack, etc.).

[0144] The application environment (e.g., a mobile application environment, a web application environment, etc.) may support one or more of the following: a dietary intake log that can receive input from the patient and / or automatically track intake by the patient (e.g., a food log, a drink log, etc.) (e.g., in conjunction with applications supported by Apple Health™, Google Health™, etc.); medication use; a personalized diet and fitness plan or any other parameter known or generated from a personalized intervention, treatment, management, and / or monitoring plan. Additionally or alternatively, the application environment may include an interface for connection (e.g., using Bluetooth™, using another protocol) with a smart device (e.g., as described herein, for automatic weighing, tracking cardiovascular parameters, tracking blood analyte parameters, motion tracking, etc.).

[0145] The application environment may further support telehealth interactions between patients and counseling / healthcare delivery clinicians. For example, the application environment may provide one or more of: a communication interface that connects patients with physicians and / or facilitates the delivery of care to patients (e.g., by automatically processing insurance claims, by generating appointments, by enabling consultations with clinicians, etc.); a communication interface that provides constant or near-constant access (e.g., 24 / 7 access, etc.) to trained coaches, nutritionists, counselors, etc.; a communication interface that enables telehealth group coaching; exercise regimen components (e.g., group fitness content, exercise guidance content, yoga content, etc.); stress management educational materials (e.g., provided to patients in response to detected stress states and / or trigger events, provided to patients to allow them to conveniently access content, etc.); an interface to a personal (e.g., invitation-only) social network or community; tasks (e.g., health habit challenges); an interface for providing rewards (e.g., community celebration events, incentives, other perks, etc.); and other suitable interfaces. However, the application environment may support other suitable functionality related to personalized intervention, treatment, management and / or monitoring plans.

[0146] Column (I) refers to executing an individualized intervention, treatment, management, and / or monitoring plan for the patient, where executing the individualized intervention, treatment, management, and / or monitoring can involve executing components of the intervention, treatment, management, and / or monitoring via interfaces described herein and / or with respect to the systems described below. As such, executing can involve a mobile device application interface, a web application interface, an interface with an entity (e.g., a human care delivery entity, a digital care delivery entity, etc.), and / or other suitable interface.

[0147] Implementation of the personalized intervention, treatment, management and / or monitoring plan can be based on one or more individual, multiple, or aggregated of financial (e.g., cost of care, cost of medication, etc.), prevalence (e.g., percent risk for heart attack, etc.), laboratory testing (e.g., DNA methylation levels, changes in inflammation levels, etc.), imaging (e.g., FFR from CCTA, etc.), medical (e.g., risk score, data from electronic medical records, etc.), clinical (e.g., blood pressure levels, presence of symptoms such as chest pain, etc.), comorbidities (e.g., obesity, diabetes, hypertension, hypercholesterolemia, etc.), treatments (e.g., types of statins available, etc.), supplements (e.g., vitamin D, vitamin B12, iron, etc.), lifestyle (e.g., smoking, alcohol consumption, frequency of exercise, etc.), or other suitable data.

[0148] Implementation of the individualized intervention, treatment, management and / or monitoring plan results in outcomes for patients participating in each individualized intervention, treatment, management and / or monitoring plan and / or other users (e.g., employers, payers, etc.), examples of which include improved engagement (e.g., enrollment of 93% of participants; consistent engagement by a significant percentage of participants after 60 days); improved outcomes (e.g., significant weight loss improvement, significant reduction in A1C levels associated with diabetes, significant reduction in cardiovascular symptoms, changes in DNA methylation biomarkers associated with cardiovascular disease risk, condition or comorbidity, reduction in mortality, etc.); reduced use / need for medication; reduction in health care costs; reduction in events (e.g., reduction in emergency room visits, reduction in number of heart attacks, etc.) and other suitable benefits.

[0149] Additionally or alternatively, improved outcomes can be measured at an individual level, or by aggregate number of patients in a cohort, or compared to previous levels, or compared to another cohort, and can include percent weight loss, weight loss over time, mean number of months maintaining weight loss after completing the program, mean reduction in HbA1C levels, mean reduction in fasting blood glucose, percent change in DNA methylation, direction of DNA methylation change, among other improved outcomes.

[0150] Executing the personalized intervention, treatment, management, and / or monitoring plan in column (I) can include providing results presented in one or more sections of one or more reports generated from the model output (e.g., within the application environment), determined based on one or more markers of the same or different types. The reports can then be transmitted to the involved entities (e.g., patient, caregiver, insurance company, etc.) via a mobile application and / or web application architecture. Executing the personalized intervention, treatment, and / or monitoring plan in column (I) can further implement the individual marker and / or aggregate marker profile to guide the patient's precision care course and / or monitoring and / or management and / or treatment and / or coaching.

[0151] Implementing the personalized intervention, treatment, management, and / or monitoring plan in column (I) can further include providing a personalized care program that implements physical endpoints, clinical endpoints, lifestyle endpoints, epigenetic and genetic profiles, and personalized health coaching to manage targeted outcomes (e.g., weight loss, reduced risk of heart attack). The personalized care program can provide the patient with digital tools to track lifestyle and wellness markers (e.g., blood pressure, heart rate, weight, sleep, hunger, cravings, stress, meditation, superfoods, energy, foods to avoid, exercise, etc.) and describe the patient's habits and markers (e.g., via a photo journal, via a text journal, via a wearable device, via an app, etc.), and assign the patient a health coach who works personally with the patient through guided sessions scheduled by the patient to interpret the patient's tracking and personalized report generated from the data. In accordance with the personalized care program, the report can provide a breakdown of the endpoints (e.g., obesity risk, etc.) being tracked based on the individual's data and measurements (e.g., epigenetic profile, age, etc.). The program can be tailored toward one or more goals (e.g., reducing a patient's blood pressure by 5% within 90 days of starting the program, reducing the number of employee visits to the emergency room, etc.) To achieve this goal, an implementation of the program can include automated and manual tools to motivate participants to make incremental lifestyle changes, reminders to track progress, and tools to track progress.

[0152] In specific examples, implementation of an individualized intervention, treatment, management, and / or monitoring plan based on the described model and / or data output may not incorporate all elements, such as a health coach, or similar elements may be substituted (e.g., a clinician instead of a health coach), or other elements may be added (e.g., a clinician in addition to a health coach). Additionally, outcomes measured during implementation of the individualized intervention, treatment, management, and / or monitoring plan may be used to repeat, optimize, or change the individualized intervention, treatment, management, and / or monitoring plan.

[0153] In specific examples, implementation of individualized interventions, treatments, management and / or monitoring plans based on the described models and / or data outputs can be used to generate financial (e.g., cost of care, cost of medication, etc.), prevalence (e.g., percent of employees at risk for heart attack, etc.), laboratory testing (e.g., changes in DNA methylation levels, inflammation levels, etc.), imaging (e.g., FFR from CCTA, etc.), medical (e.g., changes in endpoints reported to electronic medical records, etc.), clinical (e.g., changes in blood pressure, reduction in severity of symptoms such as chest pain, etc.), comorbidities (e.g., obesity, diabetes, hypertension, Individual, multiple, aggregate, comparative or concurrent changes in one or more of the following parameters can be assessed quantitatively or qualitatively for a patient or group of patients or a user or group of users at baseline or compared to a baseline state: a patient's risk of heart disease, stroke, stroke, stroke severity, or stroke severity; a patient's risk of heart disease ...

[0154] The set of cardiovascular symptoms can include one or more of pain levels (e.g., chest pain level, limb pain level, neck pain level, sore throat level, abdominal pain level, back pain level, etc.), chest tightness, chest pressure, angina pectoris, respiratory distress (e.g., shortness of breath, blood oxygen level, respiratory rate, etc.), numbness in the limbs, limb strength, vascular constriction, cardiovascular parameters (e.g., heart rate, heart rate variability, blood pressure, cardiac output, electrocardiogram signature, stroke volume, cardiac index, etc.), cholesterol levels (e.g., HDL levels), blood glucose levels, HbA1c levels, percent stenosis, level of obstruction, fractional flow reserve, DNA methylation value, gene profile, and other cardiovascular symptoms. The cardiovascular symptoms can be associated with one or more of coronary artery disease, coronary heart disease, hypertension, cardiac arrest, congestive heart failure, arrhythmia, peripheral arterial disease, stroke, congenital heart disease, and / or other diseases or comorbidities.

[0155] The methods described herein can produce changes in one or more of the factors outlined herein, individually and / or simultaneously, in less than 30 days, 30-60 days, 60-90 days, 90-120 days, any intermediate number of days, or more than 120 days. Variations of the methods described can produce changes in one or more of the factors outlined herein, individually and / or simultaneously, in at least 0.1% of patients.

[0156] Additionally or alternatively, the methods described herein can be used to assess changes to one or more of the factors outlined herein, individually and / or simultaneously, at one time (e.g., at a single time point), or at several time points (e.g., at random time points, at periodic time points, in relation to a trigger event, at other frequencies, etc.).

[0157] While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or what may be claimed, but rather as descriptions of features specific to particular implementations of particular inventions. Certain features described herein in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Moreover, features may be described herein as working in certain combinations, and although originally claimed as such, one or more features from the claimed combination may, in some cases, be deleted from the combination, and the claimed combination may be directed to a subcombination or a variation of the subcombination.

[0158] Although this document presents certain implementations of the methods and compositions described herein for illustrative purposes, it should be recognized by those skilled in the art that these examples are non-limiting. Those with the necessary knowledge may make a range of modifications, changes, and substitutions without departing from the essence of the methods and compositions. It is recognized that those skilled in the art may devise various alternatives to the disclosed embodiments.

[0159] Similarly, although the figures show operations in a particular order, this should not be understood as requiring such operations to be performed in the particular order or sequential order shown, or that all of the illustrated operations must be performed, to achieve desired results. In certain situations, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described herein should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems may generally be integrated together in a single software product or packaged in multiple software products.

[0160] Accordingly, certain implementations of the subject matter have been described. Other implementations are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown or sequential order to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.

Claims

1. generating, by the computer system, a user interface for display on a display device operatively coupled to the computer system; generating, by a computer system, first data comprising test results of genetic biomarkers, epigenetic biomarkers, and / or other biomarkers for the patient to assess the presence or absence of a disease, disorder, or risk in the patient; displaying a user interface on a display device; and Displaying the first data in the user interface A method comprising:

2. 2. The method of claim 1, wherein the data is selected from measurements (e.g., weight, blood pressure, etc.), test results (e.g., genetic assays, epigenetic assays, protein assays, metabolic assays, etc.), monetary (e.g., cost of procedures, cost of medications, claims costs, etc.), industrial / prevalence (e.g., prevalence of heart disease in a particular geographic area, number of heart attacks per year among truck drivers, etc.), interventions (e.g., surgical procedures, etc.), medical (e.g., ICD10 codes, medication use, etc.), clinical (e.g., blood pressure, etc.), treatments (e.g., cost of statins, drugs that reduce inflammation, etc.), lifestyle (e.g., smoking, alcohol consumption, exercise frequency, etc.), supplement use (e.g., herbs, vitamin D, vitamin B12, iron, etc.), electronic data (e.g., imaging, electronic health records, publicly available data (e.g., repositories, etc.), publications, formulations, clinical trials, etc.) and diagnostic, interventional, management, monitoring, and / or therapeutic significance thereof.

3. 3. The method of claim 1 or 2, wherein the data includes standalone data, aggregated data, complementary data, derived data, numerical data, text data, image data, and combinations thereof.

4. 4. The method of claim 1, wherein the data is displayed in one or more plots in a user interface.

5. The method of any one of claims 1 to 4, wherein the data is compared with test results of corresponding genetic biomarkers, epigenetic biomarkers, and / or other biomarkers from a cohort population.

6. 6. The method of any one of claims 1 to 5, wherein the data is overlaid onto test results for genetic, epigenetic and / or other biomarkers for a population.

7. 7. The method of any one of claims 1 to 6, wherein the user interface displays diagnostic information, prognostic information, uncertainty quantification, clinical and / or business risk management or decision-making, regulatory and / or policy-making, intervention, monitoring and / or treatment decisions and strategies for a patient or group of patients or a user or group of users.

8. 8. The method of any one of claims 1-7, wherein the genetic, epigenetic, and / or other biomarker test results for the patient population comprise a bar graph showing the age of the patient population on the X-axis and the probability that patients of different ages have the disease, disorder, or risk on the Y-axis, and wherein the test results for the patients comprise the probability that the patient has the disease.

9. 10. The method of any one of claims 1 or 8, wherein the patient is age-related, and the method further comprises displaying the probability that the patient has the disease adjacent to the probability for a population of patients of the same age as the patient.

10. 10. The method of any one of claims 1 to 9, wherein the patient is associated with a gender, and the method further comprises displaying the probability that the patient has the disease adjacent to the probability for a population of patients of the same gender as the patient.

11. receiving a ranking of the contributions of a plurality of genetic biomarkers, epigenetic biomarkers, and / or other biomarkers to the presence of disease in the patient; generating a chart including the names of the plurality of markers on the X-axis and a normalized ranking of the contributions of the plurality of genetic, epigenetic and other biomarkers for the patient; overlaying the chart with another chart showing a normalized ranking of the contributions of multiple genetic, epigenetic, and / or other biomarkers to the patient population, resulting in a second plot of the rankings for the markers; and Displaying a second plot adjacent to the plot in the user interface.

11. The method of any one of claims 1 to 10, further comprising:

12. generating a plot of the distribution of measurements of the genetic biomarkers, epigenetic biomarkers, and / or other biomarkers relative to a particular genetic biomarker, epigenetic biomarker, and / or other biomarker; overlaying the plot with measurements of genetic, epigenetic, and / or other biomarkers measured for the patient; and Displaying the plot in a user interface with overlaid genetic, epigenetic, and / or other biomarker measurements.

12. The method of any one of claims 1 to 11, further comprising:

13. Determining upper and lower uncertainty bounds associated with the measurements of genetic, epigenetic, and / or other biomarkers measured for the patient; and displaying within the plot upper and lower limits that bound the measurements of the genetic, epigenetic, and / or other markers.

13. The method of any one of claims 1 to 12, further comprising:

14. detecting a selection of data displayed within the user interface; In response to detecting the selection, displaying a window adjacent to the data in the user interface; and Displaying hyperlinks to further information in the window 14. The method of any one of claims 1 to 13, further comprising:

15. 15. The method of claim 14, wherein the additional information comprises literature regarding test results for genetic biomarkers, epigenetic biomarkers, and / or other biomarkers, or literature regarding diseases, disorders, or risks.

16. 16. The method of any one of claims 1 to 15, wherein the literature is selected from publications, clinical data, clinical trials, and formulations.

17. A computer-readable medium storing computer instructions that, when executed by one or more computer processors, are configured to cause the one or more computer processors to perform operations including the method of any one or more of claims 1 to 16.

18. one or more computer processors; and A computer readable medium storing computer instructions that, when executed by said one or more processors, cause said one or more processors to perform operations including the method of any one or more of claims 1 to 16. A computer system comprising:

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