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Psychiatric profiles of eHealth users evaluated using data mining techniques: cohort study
| dc.contributor.author | Lopez-Castroman, Jorge | |
| dc.contributor.author | Abad-Tortosa, Diana | |
| dc.contributor.author | Cobo Aguilera, Aurora | |
| dc.contributor.author | Courtet, Philippe | |
| dc.contributor.author | Barrigón-Estévez, María L. | |
| dc.contributor.author | Artés-Rodríguez, Antonio | |
| dc.contributor.author | Baca-Garcia, Enrique | |
| dc.date.accessioned | 2022-07-08T18:30:14Z | |
| dc.date.available | 2022-07-08T18:30:14Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/3892 | |
| dc.description.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. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | JMIR Mental Health, 8(1), e17116 | es_CL |
| dc.subject | Data mining | es_CL |
| dc.subject | Digital phenotyping | es_CL |
| dc.subject | Mental disorders | es_CL |
| dc.subject | Suicidal ideation | es_CL |
| dc.subject | Suicide prevention | es_CL |
| dc.title | Psychiatric profiles of eHealth users evaluated using data mining techniques: cohort study | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias de la Salud | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.indexacion | Isi | es_CL |
| dc.ucm.uri | mental.jmir.org/2021/1/e17116/ | es_CL |
| dc.ucm.doi | doi.org/10.2196/17116 | es_CL |
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