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

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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, DeRubeis, Robert, Iniesta, Raquel, Dwyer, Dominic, Choi, Karmel·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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The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Educating the reflective practitioner
Schön, D. A. (1987) · Cited by 11,335
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Toward a Theory of Situation Awareness in Dynamic Systems
Endsley, M. R. (1995) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872095779049543 · Cited by 8,875
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1×
“For example, a widely used machine learn ing algorithm assigned the same level of disease risk to Black and White patients, even if Black patients were sicker than White patients 210 .”
Background
Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report
Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., Niederehe, G., Thase, M. E., Lavori, P. W., Lebowitz, B. D., et al. (2006) · American Journal of Psychiatry · doi:10.1176/ajp.2006.163.11.1905 · Cited by 5,643
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340

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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
Major depressive disorder
Marx, W., Penninx, B. W. J. H., Solmi, M., Furukawa, T. A., Firth, J., Carvalho, A. F., & Berk, M. (2023) · Nature Reviews Disease Primers · doi:10.1038/s41572-023-00454-1 · Cited by 910
Enhancing mental health with Artificial Intelligence: Current trends and future prospects
Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F. T., & Eberhardt, J. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100099 · Cited by 667
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Stade, E. C., Stirman, S. W., Ungar, L. H., Boland, C. L., Schwartz, H. A., Yaden, D. B., Sedoc, J., DeRubeis, R. J., Willer, R., & Eichstaedt, J. C. (2024) · npj Mental Health Research · doi:10.1038/s44184-024-00056-z · Cited by 391
“Data-drivenapproachesholdthepromiseofrevealingpatternsthatare not yet realized by clinicians, thus generating new approaches to psychotherapy; machine learning is already being used, for example, to predict behavioral health treatment outcomes 54 .”
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Stein, D. J., Shoptaw, S. J., Vigo, D. V., Lund, C., Cuijpers, P., Bantjes, J., Sartorius, N., & Maj, M. (2022) · World Psychiatry · doi:10.1002/wps.20998 · Cited by 331
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Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
Meehan, A. J., Lewis, S. J., Fazel, S., Fusar-Poli, P., Steyerberg, E. W., Stahl, D., & Danese, A. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01528-4 · Cited by 219
Digital transformation of mental health services
Bond, R. R., Mulvenna, M. D., Potts, C., O’Neill, S., Ennis, E., & Torous, J. (2023) · npj Mental Health Research · doi:10.1038/s44184-023-00033-y · Cited by 199
AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling
Kale, M. B., Wankhede, N. L., Pawar, R. S., Ballal, S., Kumawat, R., Goswami, M., Khalid, M., Taksande, B. G., Upaganlawar, A. B., Umekar, M. J., et al. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102497 · Cited by 208
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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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