SELF-IMPROVING METHOD OF USING ONLINE COMMUNITIES TO PREDICT HEALTH-RELATED OUTCOMES

• US 20090131758A1
• Filed: 10/14/2008
• Published: 05/21/2009
• Est. Priority Date: 10/12/2007
• Status: Active Grant

• Current Assignee: PatientsLikeMe LLC (UnitedHealth Group Incorporated)
• Original Assignee: PatientsLikeMe, Inc. (UnitedHealth Group Incorporated)
• Inventors: Heywood, James, Heywood, Benjamin
• Granted Patent: US 9,589,104 B2
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/4824 Touch or pain perception evaluation A61B5/4821 takes precedence
• CPC Class Codes: A61B 5/4833 Assessment of subject's compliance to treatment
• CPC Class Codes: G06Q 50/22 Social work or social welfare, e.g. community support activities or counselling services
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/60 relating to pathologies
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention is directed, in part, to method of using self-reported health data in online communities to predict significant health events in life-changing illnesses to improve the lives of individuals and to improve patient self-management. The invention provides a method for providing real-time personalized medical predictions for an individual patient. The method includes: providing a database containing patient information for a plurality of other patients including one or more attributes for each patient in the database; constructing a model of a disease based on disease progressions for the plurality of patients; receiving a request from the individual patient, the patient associated with one or more attributes; and making a real-time prediction for the individual patient based on the mode and the individual patient'"'"'s attributes.

Claims

1. A method for providing real-time personalized medical predictions for an individual patient, the method comprising:

providing a database containing patient information for a plurality of other patients including one or more attributes for each patient in the database;

constructing a model of a disease based on disease progressions for the plurality of patients;

receiving a request from the individual patient, the patient associated with one or more attributes; and

making a real-time prediction for the individual patient based on the mode and the individual patient'"'"'s attributes.

2. The method of claim 1, wherein the one or more attributes includes at least one selected from the group consisting of:

age, race, ethnicity, gender, height, weight, body mass index (BMI), body volume index (BVI), genotype, phenotype, severity of the disease, progression rate of the disease, measures of functional ability, quality of life, interventions, and remedies.

3. The method of claim 1, wherein the disease includes at least one selected from the group consisting of:

neurological diseases, Amytrophric Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Parkinson'"'"'s Disease, Human Immunodeficiency Virus (HIV), Acquired Immune Deficiency Syndrome (AIDS), depression, mood disorders, cancer blood cancer, fibromyalgia, epilepsy, post traumatic stress disorder, traumatic brain injury, cardiovascular disease, osteoporosis, chronic obstructive pulmonary disease, arthritis, allergies, autoimmune diseases, and lupus.

4. The method of claim 1, wherein the data returned includes individual data for one or more members of the set of other patients.

5. The method of claim 1, wherein the data returned includes aggregate data for one or more members of the set of other patients.

6. The method of claim 5, further comprising:

processing a request from the patient to view individual data.

7. The method of claim 1, wherein the model is based on data for a subset of the plurality of patients, and the method further comprises:

processing a request from the patient to modify a composition of the subset of the plurality of patients.

8. The method of claim 7, wherein the composition of the subset of other patients is defined by fuzzy logic.

9. The method of claim 7, wherein modifying the composition of the subset of the plurality of patients includes modifying the range of attributes of patients within the subset.

10. The method of claim 7, wherein modifying the composition of the subset of the plurality of the patients includes modifying the importance of attributes of patients in composing the subset.

11. The method of claim 1, further comprising:

conducting a multivariate pattern matching search of data related to the plurality of patients.

12. The method of claim 1, further comprising:

calculating a confidence interval for the prediction.

13. The method of claim 12, wherein calculating a confidence interval for the prediction constitutes:

selecting a set of reported data points from the plurality of other patients;

for each of the reported data points in the set;

obtaining a data set for the corresponding other patient to the reported data point;

calculating a predicted value with the data set and the model; and

calculating an error between the predicted value and the reported data point;

producing a distribution of the errors; and

calculating a confidence interval from the distribution.

14. The method of claim 13, wherein the set of reported data points includes n closest reported data points to the prediction.

15. The method of claim 13, wherein the set of reported data points includes reported data points within an ellipsoid defined by a distance metric.

16. The method of claim 13, wherein a size of the data set for the corresponding other patient is comparable to a quantity of attributes associated with the individual patient.

17. The method of claim 1, further comprising:

analyzing an effect of an intervention by measuring a difference between a prediction absent the intervention and a reported outcome with the intervention.

18. The method of claim 17, wherein the difference is measured for a plurality of individual patients.

19. The method of claim 17, wherein the difference is compared to the distribution of error.

20. The method of claim 17, wherein the difference is compared to the confidence interval for the model.

21. The method of claim 20, further comprising:

identifying one or more of the differences that exceed the confidence interval for the model.

22. The method of claim 18, further comprising:

assembling a distribution of the differences for the plurality of individual patients; and

computing a standard error for the distribution.

23. The method of claim 12, wherein the confidence interval is calculated with a chi-square test.

24. The method of claim 12, wherein the confidence interval is calculated from a measure of variance of the individual patient'"'"'s attributes.

25. The method of claim 12, wherein the confidence interval is calculated by comparing the individual patient'"'"'s attributes to a model fit for the individual patient using the model.

26. A computer-readable medium whose contents cause a computer to perform a method for providing real-time personalized medical predictions for an individual patient, the method comprising:

providing a database containing patient information for a plurality of other patients including one or more attributes for each patient in the database;

constructing a model of a disease based on disease progressions for the plurality of patients;

receiving a request from the individual patient, the patient associated with one or more attributes; and

making a real-time prediction for the individual patient based on the mode and the individual patient'"'"'s attributes.

27. A method for providing real-time personalized medical predictions, the method comprising:

gathering patient-submitted information from a community of patients having a disease, the information including medical condition metrics and intervention data;

utilizing the patient-submitted information to form a model of the disease; and

predicting the progression of the disease in a particular patient by applying information submitted by an individual patient to the model.

28. The method of claim 27, wherein the information submitted by the individual patient includes a date of onset of the disease.

29. The method of claim 27, wherein the step of predicting the progression of the disease is performed in real time.

30. The method of claim 27, wherein the step of predicting the progression of the disease including providing a confidence interval.

31. The method of claim 27, wherein the step of predicting the progression of the disease including providing a graphical prediction.

32. The method of claim 27, wherein the graphical prediction is a line chart depicting development of the disease with regard to a rating scale.

33. The method of claim 27, wherein the disease includes at least one selected from the group consisting of:

neurological diseases, Amytrophric Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Parkinson'"'"'s Disease, Human Immunodeficiency Virus (HIV), Acquired Immune Deficiency Syndrome (AIDS), depression, mood disorders, cancer blood cancer, fibromyalgia, epilepsy, post traumatic stress disorder, traumatic brain injury, cardiovascular disease, osteoporosis, chronic obstructive pulmonary disease, arthritis, allergies, autoimmune diseases, and lupus.

34. The method of claim 27, wherein the model is a model of the disease'"'"'s pathology.