Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data
Open accessAbstract
Major depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42-53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm's capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm's citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p's < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.
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MDD is poorly defined and diagnosed because its diagnosis is mostly based on data subjectively reported by patients themselves and lacks objective, clinically relevant outcome measures.
“Despite its medical importance MDD is poorly defined and diagnosed since its diagnosis is mostly based on data subjectively reported by the patients themselves (13–15) and lack of objective, clinically relevant outcome measure.”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [15]
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- Authors
- Taliaz, D., Spinrad, A., Barzilay, R., Barnett-Itzhaki, Z., Averbuch, D., Teltsh, O., Schurr, R., Darki-Morag, S., & Lerer, B.
- Affiliations
- Children's Hospital of Philadelphia (US)
- Hebrew University of Jerusalem (IL)
- Corresponding author
- Taliaz, Dekel
- Lerer, Bernard
- ORCID
- Venue
- Translational Psychiatry
- Published
- 2021-07-08
- Volume
- 11
- Issue
- 1
- Pages
- 381-381
- ISSN
- 2158-3188 (ISSN-L)
- PMC
- PMC8266902
- OpenAlex
- W3180628988
- Cited by
- 93
- References
- 75
- Citations by year
- 2026: 16 · 2025: 23 · 2024: 23 · 2023: 13 · 2022: 14 · 2021: 4
- FWCI
- 7.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Translational Psychiatry (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (832230)
- Israeli Innovation Authority
- Topics
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Keywords
- antidepressant response prediction, major depressive disorder, STAR*D, pharmacogenomics, citalopram, antidepressant selection, machine learning prediction models, personalized treatment
- MeSH terms
- Machine Learning, Antidepressive Agents, Demography, Depressive Disorder, Major, Major Depressive Disorder, Humans, Citalopram, Treatment Outcome
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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