The promise of machine learning in predicting treatment outcomes in psychiatry

Chekroud, Adam M.; Bondar, Julia; Delgadillo, Jaime; Doherty, Gavin; Wasil, Akash; Fokkema, Marjolein; Cohen, Zachary; Belgrave, Danielle; DeRubeis, Robert; Iniesta, Raquel; Dwyer, Dominic; Choi, Karmel

doi:10.1002/wps.20882

Cited by 532Machine Learning in HealthcareArtificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

The promise of machine learning in predicting treatment outcomes in psychiatry

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Chekroud, Adam M., Bondar, Julia, Delgadillo, Jaime, Doherty, Gavin, Wasil, Akash, Fokkema, Marjolein, Cohen, Zachary, Belgrave, Danielle +4 more·2021·World Psychiatry

DOI 10.1002/wps.20882PMC PMC8129866

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Abstract

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.
“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
The “HOW CLOSE WE HAVE COME TO REAL-WORLD IMPLEMENTATION” section (page not specified) says black-box models can be difficult for clinicians to interpret and that accurate algorithms alone are insufficient for clinical decision support. The paper mentions text-based clinical notes, but does not say such models fail to capture ambiguity in psychiatric documentation or judicial interpretation. (LLM, conf=0.88)
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[26]26. (PReDicT): an open-label , randomised controlled trial. Neuropsychophar macology (in press).

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“[14] User: [picking the only available continuation] I see.”
Background
AI-assisted prediction of differential response to antidepressant classes using electronic health records
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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: Chekroud, Adam M.
Bondar, Julia
Delgadillo, Jaime
Doherty, Gavin
Wasil, Akash
Fokkema, Marjolein
Cohen, Zachary
DeRubeis, Robert
Iniesta, Raquel
Dwyer, Dominic
Choi, Karmel
Venue: World Psychiatry
Publisher: Wiley
Published: 2021-05-18
Volume: 20
Issue: 2
Pages: 154-170
ISSN: 1723-8617 (ISSN-L)
2051-5545
DOI: 10.1002/wps.20882
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
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
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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