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Machine learning methods in psychiatry: a brief introduction
  1. Zhirou Zhou1,
  2. Tsung-Chin Wu2,
  3. Bokai Wang1,
  4. Hongyue Wang1,
  5. Xin M Tu3,4 and
  6. Changyong Feng1
  1. 1 Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA
  2. 2 Department of Mathematics, University of California San Diego, La Jolla, California, USA
  3. 3 Family Medicine and Public Health, University of California San Diego, La Jolla, California, USA
  4. 4 Naval Health Research Center, San Diego, California, USA
  1. Correspondence to Professor Changyong Feng; Changyong_Feng{at}


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.

  • models
  • statistical
  • psychiatry

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  • Correction notice This article has been corrected since it was first published. The second equation under the section heading 'Unsupervised Learning' was missing an end parenthesis. This has since been updated.

  • Contributors ZZ and T-CW: collected the data and wrote the draft. BW and HW: reviewed and revised the draft. XMT: reviewed the article. CF: proposed the topic and reviewed the final draft.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Commissioned; internally peer reviewed.

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