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Currently submitted to: JMIR Mental Health

Date Submitted: Nov 20, 2019
Open Peer Review Period: Nov 20, 2019 - Jan 15, 2020
(currently open for review)

Psychiatric Profiles of e-health users: What can we learn using data mining techniques?

  • Jorge Lopez-Castroman; 
  • Diana Abad-Tortosa; 
  • Aurora Cobo; 
  • Philippe Courtet; 
  • Maria Luisa Barrigón; 
  • Antonio Artés-Rodríguez; 
  • Enrique Baca-García; 



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.


Please cite as:

Lopez-Castroman J, Abad-Tortosa D, Cobo A, Courtet P, Barrigón ML, Artés-Rodríguez A, Baca-García E

Psychiatric Profiles of e-health users: What can we learn using data mining techniques?

JMIR Preprints. 20/11/2019:17116

DOI: 10.2196/preprints.17116


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