Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE: Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted. OBJECTIVE: To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can...

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Detalles Bibliográficos
Autores principales: Rajpurkar, Pranav, Yang, Jingbo, Dass, Nathan, Vale, Vinjai, Keller, Arielle S., Irvin, Jeremy, Taylor, Zachary, Basu, Sanjay, Ng, Andrew, Williams, Leanne M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2020
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309440/
https://www.ncbi.nlm.nih.gov/pubmed/32568399
http://dx.doi.org/10.1001/jamanetworkopen.2020.6653

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309440/
https://www.ncbi.nlm.nih.gov/pubmed/32568399
http://dx.doi.org/10.1001/jamanetworkopen.2020.6653

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