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Psychiatric profiles of ehealth users evaluated using data mining techniques: cohort study

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URI: https://hdl.handle.net/10016/33238
ISSN: 2368-7959
DOI: https://doi.org/10.2196/17116
UXXI: AR/0000028062
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Psychiatric_JMIRMH_2021.pdf (778.07 KB)
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2021-01-20
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Background: New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps. Objective: This study aimed to reveal the profiles of users of a mental health app through machine learning techniques. Methods: We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login. Results: The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features. Conclusions: User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.
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Data mining, Digital phenotyping, Mental disorders, Suicidal ideation, Suicide prevention
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Lopez-Castroman, J., Abad-Tortosa, D., Cobo Aguilera, A., Courtet, P., Barrigón, M. L., Artés, A., & Baca-García, E. (2021). Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study. JMIR Mental Health, 8(1), e17116. https://doi.org/10.2196/17116
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