Machine learning methods in psychiatry: a brief introduction

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

doi:10.1136/gpsych-2019-100171

Machine learning methods in psychiatry: a brief introduction

Gen Psychiatr. 2020 Feb 3;33(1):e100171. doi: 10.1136/gpsych-2019-100171. eCollection 2020.

Authors

Zhirou Zhou¹, Tsung-Chin Wu², Bokai Wang¹, Hongyue Wang¹, Xin M Tu^{3

4}, Changyong Feng¹

Affiliations

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

Abstract

Machine learning (ML) techniques have been widely used to address mental health questions. We discuss two main aspects of ML in psychiatry in this paper, that is, supervised learning and unsupervised learning. Examples are used to illustrate how ML has been implemented in recent mental health research.

Keywords: models; psychiatry; statistical.