Machine learning methods in psychiatry: a brief introduction.

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

Afiliação

Zhou Z; Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.
Wu TC; Department of Mathematics, University of California San Diego, La Jolla, California, USA.
Wang B; Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.
Wang H; Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.
Tu XM; Family Medicine and Public Health, University of California San Diego, La Jolla, California, USA.
Feng C; Naval Health Research Center, San Diego, California, USA.

Gen Psychiatr ; 33(1): e100171, 2020.

Article em En | MEDLINE | ID: mdl-32090196

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.

Palavras-chave

models; psychiatry; statistical

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article