ArticleNPJ digital medicine2023
AI-assisted prediction of differential response to antidepressant classes using electronic health records.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Transformative Role of Artificial Intelligence in Drug Discovery and Translational Medicine: Innovations, Challenges, and Future Prospects.Drug design, development and therapy · 2025Pooled it
- The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review.Current topics in medicinal chemistry · 2025Pooled it
- Trial
- Review
- Nutritional Management of Schizophrenia: Gut-Brain Axis Mechanisms and Dietary Interventions.Molecular neurobiology · 2026Review
- Predicting Pharmacological Treatment Response in Migraine Using AI/ML: A Scoping Review of the Evidence and Future Directions.Pharmacotherapy · 2026Review
- Technological advancements and the use of key performance indicators in hospital management: a critical-conceptual review.Frontiers in health services · 2026Review
- Building the next frontier: Artificial intelligence in 3D-printed medicines.Biomaterials translational · 2026Review
- Digital lifelines, data landmines: digital mental health tools for regional and urban families in an age of surveillance.Frontiers in digital health · 2026Article
- The Use of Artificial Intelligence for Personalized Treatment in Psychiatry.Current psychiatry reports · 2025Review
- Reimagining Resilience in Aging: Leveraging AI/ML, Big Data Analytics, and Systems Innovation.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2025Article
- The role of large language models in self-care: a study and benchmark on medicines and supplement guidance accuracy.International journal of clinical pharmacy · 2025Article
- Artificial Intelligence for Management of Major Depression: Initial Design, Progress, and Research Plans.Alpha psychiatry · 2025Article
- Prediction of early-onset bipolar using electronic health records.Journal of child psychology and psychiatry, and allied disciplines · 2025Article
- Patients prefer human psychiatrists over chatbots: a cross-sectional study.Croatian medical journal · 2025Article
- Prediction of individual treatment allocation between electroconvulsive therapy or ketamine using the Personalized Advantage Index.NPJ digital medicine · 2025Article
- Review
- Post-marketing surveillance of anticancer drugs using natural language processing of electronic medical records.NPJ digital medicine · 2024Article
- Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine.Pharmaceutics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. Predictors were derived from both structured and unstructured EHR data and models accounted for features predictive of treatment selection to minimize confounding by indication. Outcome labels were derived through expert chart review and AI-automated imputation. Regularized generalized linear model (GLM), random forest, gradient boosting machine (GBM), and deep neural network (DNN) models were trained and their performance compared. Predictor importance scores were derived using SHapley Additive exPlanations (SHAP). All models demonstrated similarly good prediction performance (AUROCs ≥ 0.70, AUPRCs ≥ 0.68). The models can estimate differential treatment response probabilities both between patients and between antidepressant classes for the same patient. In addition, patient-specific factors driving response probabilities for each antidepressant class can be generated. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.