Method for modeling behavior and psychotic disorders

• US 10,068,060 B2
• Filed: 08/28/2015
• Issued: 09/04/2018
• Est. Priority Date: 08/16/2012
• Status: Active Grant

• Current Assignee: OrangeDot, Inc.
• Original Assignee: Ginger.io, Inc. (OrangeDot, Inc.)
• Inventors: Madan, Anmol, Moturu, Sai, Wahba, Karim, Dash, Shishir, De, Gourab
• Primary Examiner(s): Tomaszewski, Michael
• Application Number:
  US14/839,232
• Publication Number:
  US 20150370994A1
• Time in Patent Office: 1,103 Days
• Field of Search: None
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• 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 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method and system for modeling behavior and a psychotic disorder-related state of a patient, the method comprising: receiving a log of use dataset associated with communication behavior of the patient during a time period; receiving a supplementary dataset characterizing mobility-behavior of the patient during the time period; generating a predictive model based upon a passive dataset derived from the log of use dataset and the supplementary dataset; transforming at least one of the passive dataset and an output of the predictive model into an analysis of a psychotic episode-risk state of the individual associated with at least a portion of the time period; and upon detection that parameters of the psychotic episode-risk state satisfy at least one threshold condition, automatically initiating provision of a therapeutic intervention for the individual by way of at least one of the computing system and the mobile communication device.

Claims

1. A method for improving psychotic disorder-related state determination for a patient, the method comprising:

transmitting, from a communication module executing on a mobile communication device to a computing system, a log of use dataset associated with communication behavior of the patient during a time period;

at the computing system, receiving a motion supplementary dataset corresponding to a motion sensor of the mobile computing device, the motion supplementary dataset characterizing mobility-behavior of the patient during the time period;

collecting GPS data corresponding to a GPS sensor of the mobile device, the GPS data associated with location behavior of the patient in association with the time period;

selecting a patient subgroup for the patient from a first subgroup and a second subgroup based on the GPS data and the motion supplementary dataset, wherein the first subgroup is selected in response to the GPS data and the motion supplementary dataset indicating a first mobility behavior associated with the first subgroup, wherein the second subgroup is selected in response to the GPS data and the motion supplementary dataset indicating a second mobility behavior associated with the second subgroup, and wherein selection of the patient subgroup is operable to improve data storage, data retrieval, and the psychotic disorder-related state determination;

at the computing system, generating a predictive model based upon the selected patient subgroup and a passive dataset derived from the log of use dataset and the motion supplementary dataset;

transforming at least one of the passive dataset and an output of the predictive model into an analysis of a psychotic episode-risk state of the patient associated with at least a portion of the time period; and

upon detection that parameters of the psychotic episode-risk state satisfy at least one threshold condition, automatically initiating provision of a therapeutic intervention for improving a health outcome of the patient, by way of at least one of the computing system and the mobile communication device.

2. The method of claim 1, further including:

providing, by way of an application of the mobile communication device, at least one of a set of psychosis-assessment surveys to the patient; and

at the computing system, receiving a survey dataset including responses, to at least one of the set of psychosis-assessment surveys, associated with a set of time points of the time period.

3. The method of claim 2, wherein providing at least one of a set of psychosis-assessment surveys includes automatically providing a first portion of a survey within the application at a first subset of time points of the time period, and providing a second portion of the survey within the application at a second subset of time points of the time period.

4. The method of claim 2, wherein the survey dataset includes responses to a Brief Psychiatric Rating Scale (BPRS)-derived survey, a mood information survey, and a sleep information survey, and wherein generating the predictive model comprises generating the predictive model from a) the survey dataset and b) the passive dataset.

5. The method of claim 4, wherein generating the analysis includes determining an amount of sleep per night of the patient over a portion of the time period from the sleep information survey, and generating an alert configured to initiate provision of the therapeutic intervention, upon detection that the amount of sleep is below a threshold amount and that responses to the BPRS-derived survey contribute to a BPRS score above a threshold score.

6. The method of claim 1, wherein transmitting the log of use dataset further includes:

at the computing system, extracting, from the log of use dataset;

a number of incoming text messages and a number of outgoing text messages.

7. The method of claim 6, wherein receiving the motion supplementary dataset includes extracting at least one of a location of the patient and mobility-related parameter associated with at least one time point of the time period and derived from the GPS sensor of the mobile communication device.

8. The method of claim 7, wherein generating the analysis includes generating a first comparison between a) a first threshold condition, associated with reduced communication behavior of the patient across a portion of the time period, and b) at least one passive data element of the log of use dataset and the supplementary dataset.

9. The method of claim 1, wherein generating the predictive model includes:

at the computing system, implementing a factor analysis approach that models changes in text messaging behavior of the patient across at least a portion of the time period.

10. The method of claim 1, wherein automatically initiating provision of the therapeutic intervention includes enabling a healthcare entity to contact the patient within an application executing at the mobile computing device, thereby preventing the patient from trending toward psychotic episode-risk state.

11. A method for improving psychotic disorder-related state determination for a patient, the method comprising:

transmitting, from a communication module executing on a mobile communication device to a computing system, a log of use dataset associated with communication behavior of the patient during a time period;

at the computing system, receiving a mobility sensor supplementary dataset corresponding to a mobility sensor of the mobile communication device, the mobility sensor supplementary dataset characterizing physical activity of the patient during the time period;

selecting a patient subgroup for the patient from a first subgroup and a second subgroup based on the mobility sensor supplementary dataset, wherein the first subgroup is selected in response to the mobility sensor supplementary dataset indicating a first mobility behavior associated with the first subgroup, wherein the second subgroup is selected in response to the mobility sensor supplementary dataset indicating a second mobility behavior associated with the second subgroup, and wherein selection of the patient subgroup is operable to improve data storage, data retrieval, and the psychotic disorder-related state determination;

at the computing system, generating a predictive model of a psychotic episode-risk state of the patient associated with at least a portion of the time period, based on the selected patient subgroup and at least one of the log of use dataset and the supplementary dataset; and

by way of at least one of the computing system and the mobile communication device, initiating provision of a therapeutic intervention for improving a health outcome of the patient, upon detection that a set of parameters outputted from the predictive model of the psychotic episode-risk state satisfy at least one threshold condition.

12. The method of claim 11, further comprising:

providing, by way of an application of the mobile communication device, at least one of a set of psychosis-assessment surveys to the patient; and

at the computing system, receiving a survey dataset including responses, to at least one of the set of psychosis-assessment surveys, associated with a set of time points of the time period.

13. The method of claim 12, wherein the survey dataset includes responses to a Brief Psychiatric Rating Scale (BPRS)-derived survey and a sleep information survey, and wherein generating the predictive model comprises generating the predictive model from a) the survey dataset and b) a passive dataset including the log of use dataset and the supplementary dataset.

14. The method of claim 11, wherein transmitting the log of use dataset further includes:

at the computing system, extracting, from the log of use dataset;

a number of incoming text messages and a number of outgoing text messages.

15. The method of claim 14, wherein initiating provision of the therapeutic intervention comprises initiating provision of the therapeutic intervention upon detection that at least one of:

a) the number of incoming text messages and b) the number of outgoing text messages is below a threshold number of text messages.

16. The method of claim 11, wherein generating the predictive model includes:

at the computing system, implementing an analytical approach that models changes in text messaging behavior of the patient across at least a portion of the time period.

17. The method of claim 16, wherein generating the predictive model includes processing the log of use dataset and the supplementary dataset with a correlation-based feature selection (CFS) algorithm to extract features of the psychotic episode-risk state across the time period.

18. The method of claim 11, wherein generating the predictive model of the psychotic episode-risk state of the patient includes generating an anticipated psychosis-related state of the user at a future time point outside of the time period, and wherein the method further includes automatically initiating provision of a therapeutic intervention for the patient to prevent the anticipated psychosis-related state of the patient.

19. The method of claim 18, wherein the anticipated psychosis-related state is a schizophrenic state, and wherein automatically initiating provision of the therapeutic intervention includes enabling a healthcare entity to contact the patient proximal in time to the future time point.

20. The method of claim 11, wherein initiating provision of the therapeutic intervention comprises rendering the alert at a user interface of a dashboard of a healthcare entity associated with the patient, and wherein rendering the alert further includes providing the healthcare entity with an electronically-implemented tool for contacting the patient.