Advanced machine learning methods in psychiatry: an introduction

Tsung-Chin Wu; Zhirou Zhou; Hongyue Wang; Bokai Wang; Tuo Lin; Changyong Feng; Xin M Tu

doi:10.1136/gpsych-2020-100197

Advanced machine learning methods in psychiatry: an introduction

Gen Psychiatr. 2020 Mar 16;33(2):e100197. doi: 10.1136/gpsych-2020-100197. eCollection 2020.

Authors

Tsung-Chin Wu¹, Zhirou Zhou², Hongyue Wang³, Bokai Wang², Tuo Lin⁴, Changyong Feng², Xin M Tu^{5

6}

Affiliations

¹ Department of Mathematics, University of California San Diego, La Jolla, California, USA.
² Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.
³ Departments of Biostatistics and Computational Biology and Anesthesiology, University of Rochester, Rochester, New York, USA.
⁴ Clinical and Translational Research Institute, University of California San Diego, San Diego, California, USA.
⁵ Family Medicine and Public Health, University of California San Diego, La Jolla, California, USA.
⁶ Naval Health Research Center, San Diego, California, USA.

Abstract

Mental health questions can be tackled through machine learning (ML) techniques. Apart from the two ML methods we introduced in our previous paper, we discuss two more advanced ML approaches in this paper: support vector machines and artificial neural networks. To illustrate how these ML methods have been employed in mental health, recent research applications in psychiatry were reported.

Keywords: mental health.