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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Detalles Bibliográficos
Autores principales: 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: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2020
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6991244/
https://www.ncbi.nlm.nih.gov/pubmed/31899530
http://dx.doi.org/10.1001/jamanetworkopen.2019.18377

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6991244/
https://www.ncbi.nlm.nih.gov/pubmed/31899530
http://dx.doi.org/10.1001/jamanetworkopen.2019.18377

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