Holdings: Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data

Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data

Background: Given that major depressive disorder (MDD) is both biologically and clinically heterogeneous, a diagnostic system integrating neurobiological markers and clinical characteristics would allow for better diagnostic accuracy and, consequently, treatment efficacy. Objective: Our study aimed...

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Main Authors:	Ho, C.S., Chan, Y.L., Tan, T.W., Tay, G.W., Tang, T.B.
Format:	Article
Published:	Elsevier Ltd 2022
Online Access:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122960799&doi=10.1016%2fj.jpsychires.2022.01.026&partnerID=40&md5=123f2b7c1ae89fa6a7092f0c8ef150f9 http://eprints.utp.edu.my/28624/
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122960799&doi=10.1016%2fj.jpsychires.2022.01.026&partnerID=40&md5=123f2b7c1ae89fa6a7092f0c8ef150f9
http://eprints.utp.edu.my/28624/

Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data

Internet

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