The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
Cross-trial prediction of treatment outcome in depression: a machine learning approach
Abstract
Background Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram.Methods We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863).Findings We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64·6% [SD 3·2]; p<0·0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59·6%, p=0.043). The model also performed significantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59·7%, p=0·023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51·4%, p=0·53), suggesting specificity of the model to underlying mechanisms.Interpretation Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant.Funding Yale University.
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“Machine learning algorithms, based on both clinical information and biomarkers, are increasingly capable of predicting treatment outcome.58”
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“Another example of an incorrect prediction by ML that could be fixed by interpretability is the case of a deep learning model that was developed to predict which patients would benefit from an antidepressant medication called escitalopram [35].”
BackgroundComputational psychiatry as a bridge from neuroscience to clinical applications
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Artificial intelligence for mental health and mental illnesses: an overview
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Metadata
- Authors
- Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R.
- Affiliations
- Yale New Haven Hospital (US)
- Capital One (United States) (US)
- Yale University (US)
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Chekroud, Adam Mourad
- ORCID
- Venue
- The Lancet Psychiatry
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 3
- Issue
- 3
- Pages
- 243-250
- ISSN
- 2215-0366 (ISSN-L)
- OpenAlex
- W2257438637
- Cited by
- 676
- References
- 49
- Citations by year
- 2026: 57 · 2025: 86 · 2024: 53 · 2023: 70 · 2022: 74 · 2021: 79 · 2020: 87 · 2019: 76 · 2018: 42 · 2017: 34 · 2016: 17
- FWCI
- 45.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- depression, antidepressant treatment response prediction, citalopram, escitalopram, clinical remission, STAR*D, venlafaxine, mirtazapine, patient-reported outcomes, treatment matching, machine learning prediction models, external validation
- MeSH terms
- Machine Learning, Adolescent, Adult, Aged, Algorithms, Antidepressive Agents, Major Depressive Disorder, Humans, Middle Aged, Models, Statistical, Citalopram, Treatment Outcome, Cross-Over Studies, Young Adult
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