Subphenotyping depression using machine learning and electronic health records

OBJECTIVE: To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications. MATERIALS AND METHODS: Using EHRs from the INSIGHT Clinical Research Network (...

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Detalles Bibliográficos
Autores principales: Xu, Zhenxing, Wang, Fei, Adekkanattu, Prakash, Bose, Budhaditya, Vekaria, Veer, Brandt, Pascal, Jiang, Guoqian, Kiefer, Richard C., Luo, Yuan, Pacheco, Jennifer A., Rasmussen, Luke V., Xu, Jie, Alexopoulos, George, Pathak, Jyotishman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Research Reports
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556423/
https://www.ncbi.nlm.nih.gov/pubmed/33083540
http://dx.doi.org/10.1002/lrh2.10241

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556423/
https://www.ncbi.nlm.nih.gov/pubmed/33083540
http://dx.doi.org/10.1002/lrh2.10241

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