How far is clinical assessment from the bullseye? Using MEmind to compare clinical assessment with self-assessment in patients with depression and anxiety diagnosis

Gómez-Carrillo, Ana; Barrigón, María Luisa; Leon-Velasco, A.; González-Garrido, Cristina; Ruiz-Gomez, Marta; Molina-Madueño, Rosa María; López-González, S.; Aroca, Fuensanta; Barahona, Igor; López-Castromán, Jorge; Berrouiguet, Sofian; Courtet, Philippe; Baca‐García, Enrique

doi:10.1016/j.ejpsy.2017.08.006

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Profile 3 was particularly overrepresented among the small group of patients with organic mental disorders (F0) in the sample, which could implicate a more complex disease course. Interestingly, in a previous paper, we found that the assessments made by clinicians did not correlate well with patients' self-reports within 24 h of a clinical evaluation [34].…”

Section: Discussionmentioning

confidence: 59%

Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study

López-Castromán¹,

Abad‐Tortosa²,

Aguilera³

et al. 2021

JMIR Ment Health

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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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Section: Discussionmentioning

confidence: 59%

Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study

López-Castromán¹,

Abad‐Tortosa²,

Aguilera³

et al. 2021

JMIR Ment Health

View full text Add to dashboard Cite

show abstract

Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study (Preprint)

López-Castromán¹,

Abad‐Tortosa²,

Aguilera³

et al. 2019

Preprint

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BACKGROUND 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. OBJECTIVE To reveal the profiles of users of a mental health app through machine learning techniques. METHODS 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. RESULTS 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. CONCLUSIONS User profiles did 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. CLINICALTRIAL

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How far is clinical assessment from the bullseye? Using MEmind to compare clinical assessment with self-assessment in patients with depression and anxiety diagnosis

Cited by 2 publications

References 17 publications

Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study

Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study

Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study (Preprint)

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