Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.
Zhdanov A, Atluri S, Wong W, Vaghei Y, Daskalakis ZJ, Blumberger DM, Frey BN, Giacobbe P, Lam RW, Milev R, Mueller DJ, Turecki G, Parikh SV, Rotzinger S, Soares CN, Brenner CA, Vila-Rodriguez F, McAndrews MP, Kleffner K, Alonso-Prieto E, Arnott SR, Foster JA, Strother SC, Uher R, Kennedy SH, Farzan F.
Zhdanov A, et al.
JAMA Netw Open. 2020 Jan 3;3(1):e1918377. doi: 10.1001/jamanetworkopen.2019.18377.
JAMA Netw Open. 2020.
PMID: 31899530
Free PMC article.
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 red …
IMPORTANCE: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effectiv …
