Currently accepted at: JMIR Mental Health
Date Submitted: Nov 20, 2019
Open Peer Review Period: Nov 21, 2019 - Jan 10, 2020
Date Accepted: Apr 5, 2020
(closed for review but you can still tweet)
PSYCHIATRIC PROFILES OF E-HEALTH USERS: WHAT CAN WE LEARN USING DATA MINING TECHNIQUES?
New technologies are changing the 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 phenotype of patients that use e-mental health.
To reveal the profiles of users of a mental health app through machine learning techniques.
We have applied a non-parametric model, the Sparse Poisson Factorization Model (SPFM), to discover latent features in the response patterns made by 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login.
The results show four different profiles of patients: 1) the common pattern for all patients included feelings of worthlessness, aggressiveness and suicidal ideas; 2) one in four reported also low energy and difficulties to cope with problems; 3) less than a quarter of the sample 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.
E-mental-health user profiles do not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, they could be useful for the prediction of behavioural risks among users of e-mental health apps.
Request queued. Please wait while the file is being generated. It may take some time.
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.