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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Dades bibliogràfiques
Publicat a:JAMA Netw Open
Autors principals: 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
Format: Artigo
Idioma:Inglês
Publicat: American Medical Association 2020
Matèries:
Original Investigation
Accés en línia: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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