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A general approach to this problem of knowledge extraction and interpretability has been to review the diagnostic criteria and the key symptoms of a mental health disorder and to look for data streams that would relate to these. In this approach, data are transformed into relevant knowledge by determining if the presence or absence of a symptom or characteristic of a disorder can be inferred from the available data.
J Med Internet Res 2021;23(8):e28918
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A cutpoint of 60 [22] was used with the LSAS scores to screen for social anxiety disorder (generalized subtype). A cutpoint of 10 [19] was used with the GAD-7 scores to screen for generalized anxiety disorder. A cutpoint of 10 [20] was used with the PHQ-8 scores to screen for depression.
Spoken words detected in the participants’ environments were collected by the smartphone app. To do so, audiorecordings were collected every 5 minutes for a duration of 15 seconds by the app.
JMIR Form Res 2021;5(1):e22723
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A general methodology in these passive EMA or mobile sensing studies, and one that is used in this work, is to compute metrics, or features, from objective data sources that are designed to capture behaviors or traits of subjects that are known or suspected to be predictive of mental state. These features condense a large number of data points from a data source (eg, thousands of GPS coordinates measured over weeks of a study) into a single metric of behavior.
JMIR Form Res 2020;4(8):e18751
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