Application US20200118458

SYSTEMS AND METHODS FOR MENTAL HEALTH ASSESSMENT
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US20200118458

1. A method for identifying whether a subject is at risk of having a mental or physiological condition, comprising: (6) (4)

(a) obtaining data from said subject, said data comprising speech data and optionally associated visual data;

(b) processing said data using one or more models comprising a natural language processing (NLP) model, an acoustic model, or a visual model, to yield processed data;

(d) outputting an electronic report identifying whether said subject is at risk of having said mental or physiological condition, based at least on said one or more features that are identified using said processed data, which said risk is quantified in a form of a score having a confidence level provided in said electronic report.

2. The method of claim 1, wherein said speech data in (a) is obtained by: (3) (2)

(i) transmitting at least one query in an audio, visual, or textual format to said subject, wherein said at least one query is configured to elicit at least one verbal response from said subject; and

(ii) receiving said speech data comprising said at least one verbal response from said subject in response to transmitting said at least one query.

3. The method of claim 2, wherein said at least one query comprises a plurality of queries and said at least one response comprises a plurality of responses, wherein said plurality of queries is transmitted in a sequential manner to said subject and configured to systematically elicit said plurality of responses from said subject. (2) (0)

4. The method of claim 3, wherein said plurality of queries is structured in a hierarchical manner such that each subsequent query of said plurality of queries is structured as a logical follow on to said subject's response to a preceding query, and is configured to assess or draw inferences on a plurality of aspects of said mental or physiological condition of said subject. (1) (0)

5. The method of claim 4, further comprising: updating said score and said confidence level using new processed data obtained from each of said subject's follow-on response to said preceding query. (2) (0)

6. The method of claim 5, further comprising: updating or assigning a clinical value to said updated score having said confidence level. (0)

7. The method of claim 5, further comprising: (1) (2)

determining whether said confidence level meets a predetermined criterion; and

generating one or more additional queries to assess or draw inferences on said plurality of aspects of said mental or physiological condition of said subject.

8. The method of claim 7, further comprising: (1) (2)

transmitting said one or more additional queries to said subject to elicit one or more additional verbal responses; and

determining whether said confidence level meets said predetermined criterion, based at least in part on said speech data comprising said one or more additional verbal responses.

9. The method of claim 8, further comprising: (0) (1)

14. The method of claim 3, further comprising: assigning a plurality of weights to said plurality of responses. (1) (0)

10. The method of claim 2, wherein said confidence level is based at least in part on a length and/or duration measure of said at least one verbal response. (0)

11. The method of claim 2, wherein said confidence level is based at least in part on an evaluated truthfulness of said at least one verbal response. (0)

12. The method of claim 1, wherein said confidence level is based at least in part on a quality measure of said speech data associated with metadata of said speech data, said subject, or a context of said speech data. (0)

13. The method of claim 1, wherein said confidence level is based at least in part on an acoustic, NLP, visual-based, or speech-recognition confidence measure of said data. (0)

16. The method of claim 1, wherein said one or more models are provided as (i) one or more individual models, (ii) jointly as two or more separate models, and/or (iii) a fused model comprising a composite model that is an aggregate of two or more different models. (0)

17. The method of claim 1, wherein said processed data comprises one or more model outputs generated from said one or more models, and wherein said one or more model outputs comprise one or more of the following: an NLP output, an acoustic output, or a visual output. (2) (0)

18. The method of claim 17, wherein said score is generated in part by taking into account at least one confidence measure associated with each of said one or more model outputs. (0)

19. The method of claim 17, wherein said score is generated at least in part by fusing two or more of said model outputs. (1) (0)

20. The method of claim 19, wherein said two or more of said model outputs comprise at least: (1) a first model output having a first confidence measure, and (2) a second model output having a second confidence measure, and wherein said confidence level of said score is generated at least in part by fusing said first confidence measure and said second confidence measure. (4) (0)

21. The method of claim 20, wherein said first model output corresponds to said NLP output, and said second model output corresponds to said acoustic output. (0)

22. The method of claim 20, wherein said first model output corresponds to said NLP output, and said second model output corresponds to said visual output. (0)

23. The method of claim 20, wherein said first model output corresponds to said acoustic output, and said second model output corresponds to said visual output. (0)

24. The method of claim 20, wherein said two or more of said model outputs further comprise at least (3) a third model output having a third confidence measure, and wherein said confidence level of said score is generated by fusing said first confidence measure, said second confidence measure, and said third confidence measure. (1) (0)

26. The method of claim 1, wherein said electronic report is usable by a user to identify whether said subject is at risk of having said mental or physiological condition. (1) (0)

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Application US20200118458

SYSTEMS AND METHODS FOR MENTAL HEALTH ASSESSMENT

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