2021
Lopez-Castroman, Jorge; Abad-Tortosa, Diana; Aguilera, Aurora Cobo; Courtet, Philippe; Barrigón, Maria Luisa; Artés-Rodríguez, Antonio; Baca-García, Enrique
Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study Artículo de revista
En: JMIR Ment Health, vol. 8, no 1, pp. e17116, 2021, ISSN: 2368-7959.
Resumen | Enlaces | BibTeX | Etiquetas: mental disorders; suicide prevention; suicidal ideation; data mining; digital phenotyping
@article{info:doi/10.2196/17116,
title = {Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study},
author = {Jorge Lopez-Castroman and Diana Abad-Tortosa and Aurora Cobo Aguilera and Philippe Courtet and Maria Luisa Barrig\'{o}n and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
url = {http://www.ncbi.nlm.nih.gov/pubmed/33470943},
doi = {10.2196/17116},
issn = {2368-7959},
year = {2021},
date = {2021-01-20},
journal = {JMIR Ment Health},
volume = {8},
number = {1},
pages = {e17116},
abstract = {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.},
keywords = {mental disorders; suicide prevention; suicidal ideation; data mining; digital phenotyping},
pubstate = {published},
tppubtype = {article}
}
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.