The DOI matches its Crossref record; 221 of the 221 references it cites were found in a registry or an index.
The promise of machine learning in predicting treatment outcomes in psychiatry
Open accessAbstract
For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.
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“Prior work in clinical decision support and mental health law has emphasized the limitations of purely statistical or black-box models in capturing the ambiguity of psychiatric documentation and judicial interpretation (Chekroud et al., 2021; McCradden et al., 2023).”
From A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support (2025) · cites this paper as Chekroud et al., 2021
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McIntyre, R. S., Alsuwaidan, M., Baune, B. T., Berk, M., Demyttenaere, K., Goldberg, J. F., Gorwood, P., Ho, R., Kasper, S., Kennedy, S. H., et al. (2023) · World Psychiatry · doi:10.1002/wps.21120 · Cited by 836
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Enhancing mental health with Artificial Intelligence: Current trends and future prospects
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Metadata
- Authors
- Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., Dwyer, D., & Choi, K.
- Affiliations
- Yale University (US)
- Spring Health New York City NY USA
- University of Sheffield (GB)
- Trinity College Dublin (IE)
- University of Pennsylvania (US)
- Leiden University (NL)
- University of California, Los Angeles (US)
- Microsoft Research (United Kingdom) (GB)
- King's College London (GB)
- Ludwig-Maximilians-Universität München (DE)
- Harvard University (US)
- ORCID
- Venue
- World Psychiatry
- Publisher
- Wiley
- Published
- 2021-05-18
- Volume
- 20
- Issue
- 2
- Pages
- 154-170
- ISSN
- 1723-8617 (ISSN-L)
- 2051-5545
- PMC
- PMC8129866
- OpenAlex
- W3160467094
- Cited by
- 531
- References
- 221
- Citations by year
- 2026: 119 · 2025: 122 · 2024: 115 · 2023: 90 · 2022: 68 · 2021: 16
- FWCI
- 25.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- World Psychiatry (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- National Institute for Health and Care Research
- NIHR Maudsley Biomedical Research Centre
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- machine learning, treatment outcome prediction, personalized treatment, psychotherapy response, digital interventions, electronic health records, genetic data, neuroimaging, external validation, clinical implementation, mental health services
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