Cargando…
Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study
BACKGROUND: Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement pat...
Autores principales: | Birnbaum, Michael Leo, Kulkarni, Prathamesh "Param", Van Meter, Anna, Chen, Victor, Rizvi, Asra F, Arenare, Elizabeth, De Choudhury, Munmun, Kane, John M |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492982/ https://www.ncbi.nlm.nih.gov/pubmed/32870161 http://dx.doi.org/10.2196/19348 |
Ejemplares similares
-
Identifying emerging mental illness utilizing search engine activity: A feasibility study
por: Birnbaum, Michael L., et al.
Publicado: (2020) -
Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook
por: Birnbaum, M. L., et al.
Publicado: (2019) -
O9.2. IDENTIFYING PSYCHOTIC SYMPTOMS AND PREDICTING RELAPSE THROUGH SOCIAL MEDIA
por: Birnbaum, Michael, et al.
Publicado: (2018) -
Designing a Clinician-Facing Tool for Using Insights From Patients’ Social Media Activity: Iterative Co-Design Approach
por: Yoo, Dong Whi, et al.
Publicado: (2020) -
A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals
por: Birnbaum, Michael L, et al.
Publicado: (2017)