System and Methods for Personal health Analytics Technical Field

• US 20150112706A1
• Filed: 10/17/2014
• Published: 04/23/2015
• Est. Priority Date: 10/17/2013
• Status: Abandoned Application

• Current Assignee: Ovuline, Inc. (Laboratory Corporation Of America Holdings)
• Original Assignee: Ovuline, Inc. (Laboratory Corporation Of America Holdings)
• Inventors: Baron, Alex, Nebesar, Gina Moro, Tofan, Vasile, Wallace, Paris
• Application Number:
  US14/517,046
• Publication Number:
  US 20150112706A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Disclosed is a system and method for providing personalized health information. The disclosed systems and methods provide for analyzing self-reported data relating to personal health information in order to predict a health condition. The systems and methods disclosed provide personalized interpretations of medical knowledge in light of the growing collection of personal health information that is publicly and privately available. Accordingly, the present disclosure provides systems and methods for personalized information intermediation to help individuals to navigate the growing selection of personal health products and services, and to contribute to health care system efficiencies by improving individual health knowledge. In some embodiments, the systems and methods disclosed provide fertility prediction encompassing a predicted date of ovulation and fertility window.

Claims

1. A method of predicting a health event, comprising:

acquiring, via a computing device, a plurality of personal datasets, each personal dataset comprising a set of self-reported health attributes from a voluntary participant of a plurality of voluntary participants;

selecting, via the computing device, from the plurality of personal datasets, a community dataset relating to a health event;

generating, via the computing device, from the community data set, a statistical model of the health event; and

estimating, via the computing device, from the statistical model, a likelihood of a personal health attribute relating to the health event for an individual participant of the plurality of voluntary participants.

2. The method of claim 1, further comprising:

obtaining a clinical guideline associated with the health event, the clinical guideline generated from a clinical dataset relating to a diagnosed health attribute;

generating, in accordance with the clinical guideline, a personalized interpretation of the estimated likelihood of the personal health attribute,

communicating, to the individual participant, information concerning the health event based on the personalized interpretation.

3. The method of claim 2, wherein the step of communicating comprises:

determining a threshold in the estimated likelihood of the personal health attribute, the threshold representing a likelihood of the personal health attribute above which a predetermined health action can be beneficial to the individual participant; and

recommending, to the individual participant, the predetermined health action when the estimated likelihood exceeds the determined threshold.

4. The method of claim 2, wherein the step of communicating comprises:

determining a personal relevance for a general collection of wellness information;

selecting a personalized collection from the general collection based on the estimated likelihood; and

providing the personalized collection to the individual.

5. The method of claim 4, wherein the personalized collection includes one of:

a personalized collection of commercial product information;

a personalized collection of commercial product offering from a third party merchant; and

a personalized collection of wellness action recommendation.

6. The method of claim 1, wherein the step of acquiring a plurality of personal datasets further comprises:

posing, periodically, a first set of questions to the voluntary participants, the first set of questions being designed to obtain a time series of variable health attributes;

posing a second set of questions to the voluntary participants, the second set of questions being designed to obtain a status of a past or current health event;

receiving a plurality of answers to the first and second set of questions; and

providing a personal feedback to the participants based on each received answer.

7. The method of claim 1, wherein

the step of generating a statistical model comprises:

extracting a sequence health attributes for an individual participant;

generating, for an individual participant, a personal statistical model from the extracted sequence health attributes; and

the step of estimating comprises;

determining a threshold in a model parameter of the generated personal statistical model, the threshold separating a sequence of health attributes likely to be associated with the personal health attribute from a sequence of health attributes unlikely to be associated with the personal health attribute;

estimating the model parameter for the individual participant according to the extracted sequence; and

classifying the individual participant according the determined threshold.

8. The method of claim 7, wherein the step of determining the threshold in the model parameter of the generated statistical model comprises:

clustering the community of voluntary participants according to a model parameter of the generated statistical model; and

determining a plurality of clusters, each cluster including one or more participants self-reporting a similar health condition or a wellness status indication, or a combination thereof

9. The method of claim 1, wherein the personal health event is selected from the group consisting of an onset of menstruation, an ovulation date, a fertility window, and a combination thereof.

10. The method of claim 9, wherein the self-reported personal health attribute is selected from the group consisting of a mood quality, a menstrual cycle type, a menstrual period length, date of last period, menstrual period dates, spotting dates, cervical fluid quality, intercourse dates, ovulation test results, pregnancy test results, body basal temperature, number of steps walked, health quality, weight, medications taken, nutrition consumed, time slept, blood pressure, activity, other relevant notes, and a combination thereof

11. The method of claim 1, wherein the personal health attribute is selected from the group consisting of a pregnancy due date, a fetal developmental milestone, fetal distress, pregnancy complications, post-partum outcome, maternal health, newborn health characteristics, gender, and a combination thereof.

12. The method of claim 9, wherein the self-reported health attributes is selected from the group consisting of number of steps walked, mood quality, health quality, weight, medications taken, nutrition consumed, time slept, blood pressure, activity, baby kick counts, contraction frequency, tagged comments, attached relevant photos, other relevant notes, and a combination thereof

13. The method of claim 2, wherein the step of generating comprises:

representing the clinical guideline in a graph; and

traversing the graph in accordance with the estimated likelihood of the personal health attribute.

14. The method of claim 7, further comprising:

obtaining a clinical guideline associated with the health event, the clinical guideline generated from a clinical dataset relating to a diagnosed health attribute;

generating, in accordance with the clinical guideline, a personalized interpretation of the estimated likelihood of the personal health attribute; and

communicating, to the individual participant, information concerning the health event based on the personalized interpretation.

15. The system of claim 14, wherein the plurality of compute stages includes:

a first compute stage for cleaning and transforming the self-reported health information into a structured representation for storing in the persistent database;

a second compute stage for naive clustering of the self-reported health information; and

a third computer stage for selecting a health status label for generating a predictive model of a health condition.

14-1. A system for providing personalized health prediction, the system comprising:

a server computing device for providing a persistent database, said server facilitating a display of a user interface for capturing self-reported health information and for storing the self-reported health information in the persistent database, said server comprising;

memory for a plurality of compute stages; and

one or more processors for executing said plurality of compute stages for accessing the self-reported health information through the server, each stage configured to increase representational structure or predictive power of the self-reported health information for predicting a personal health event.

16. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor associated with a computing device, performs a method comprising:

acquiring a plurality of personal datasets, each personal dataset comprising a set of self-reported health attributes from a voluntary participant of a plurality of voluntary participants;

selecting from the plurality of personal datasets, a community dataset relating to a health event;

generating from the community data set, a statistical model of the health event; and

estimating from the statistical model, a likelihood of a personal health attribute relating to the health event, for an individual participant of the plurality of voluntary participants