Cargando…
A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals
BACKGROUND: Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as having schizophrenia from healthy controls. To date, limited efforts have included expert input to evaluate the authenticity of diagnostic self-disclos...
Autores principales: | Birnbaum, Michael L, Ernala, Sindhu Kiranmai, Rizvi, Asra F, De Choudhury, Munmun, Kane, John M |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5575421/ https://www.ncbi.nlm.nih.gov/pubmed/28807891 http://dx.doi.org/10.2196/jmir.7956 |
Ejemplares similares
-
O9.2. IDENTIFYING PSYCHOTIC SYMPTOMS AND PREDICTING RELAPSE THROUGH SOCIAL MEDIA
por: Birnbaum, Michael, et al.
Publicado: (2018) -
Identifying emerging mental illness utilizing search engine activity: A feasibility study
por: Birnbaum, Michael L., et al.
Publicado: (2020) -
Clinician Perspectives on Using Computational Mental Health Insights From Patients’ Social Media Activities: Design and Qualitative Evaluation of a Prototype
por: Yoo, Dong Whi, et al.
Publicado: (2021) -
Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study
por: Birnbaum, Michael Leo, et al.
Publicado: (2020) -
F120. USING DIGITAL MEDIA ADVERTISING IN EARLY PSYCHOSIS INTERVENTION
por: Birnbaum, Michael, et al.
Publicado: (2018)