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The Use of Machine Learning Techniques to Solve Problems in Forensic Psychiatry

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Digital Mental Health

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Abstract

Machine learning techniques present with a great deal of promise in forensic psychiatry to predict patient outcomes at an individual level; however, the field currently remains in its infancy. The present chapter provides an overview of predictive models of criminal and violence-related outcomes in psychiatry, as well as the feigning of serious mental illness following arrest to avoid prison sentences. Moreover, this chapter details various machine learning techniques and experimental designs that can be leveraged to address long-standing problems within the field. As such, it aims to provide a series of methodological recommendations for moving the field from advancements in risk prediction towards precision forensics.

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Author information

Authors and Affiliations

  1. Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada

    Devon Watts

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  1. Devon Watts
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Editor information

Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

    Ives Cavalcante Passos

  2. Department of Social, Genetic and Developmental Psychiatry, King’s College of London, London, UK

    Francisco Diego Rabelo-da-Ponte

  3. Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada

    Flavio Kapczinski

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Watts, D. (2023). The Use of Machine Learning Techniques to Solve Problems in Forensic Psychiatry. In: Passos, I.C., Rabelo-da-Ponte, F.D., Kapczinski, F. (eds) Digital Mental Health. Springer, Cham. https://doi.org/10.1007/978-3-031-10698-9_14

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