Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression

IMPORTANCE: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient’s response to treatment could significantly reduce the burden of depressio...

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Detalhes bibliográficos
Publicado no:JAMA Netw Open
Main Authors: Zhdanov, Andrey, Atluri, Sravya, Wong, Willy, Vaghei, Yasaman, Daskalakis, Zafiris J., Blumberger, Daniel M., Frey, Benicio N., Giacobbe, Peter, Lam, Raymond W., Milev, Roumen, Mueller, Daniel J., Turecki, Gustavo, Parikh, Sagar V., Rotzinger, Susan, Soares, Claudio N., Brenner, Colleen A., Vila-Rodriguez, Fidel, McAndrews, Mary Pat, Kleffner, Killian, Alonso-Prieto, Esther, Arnott, Stephen R., Foster, Jane A., Strother, Stephen C., Uher, Rudolf, Kennedy, Sidney H., Farzan, Faranak
Formato: Artigo
Idioma:Inglês
Publicado em: American Medical Association 2020
Assuntos:
Original Investigation
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC6991244/
https://ncbi.nlm.nih.gov/pubmed/31899530
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1001/jamanetworkopen.2019.18377
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