ReviewTranslational psychiatry2019
Recommendations and future directions for supervised machine learning in psychiatry.
Review in Translational psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 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
57 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pharmacogenomic scores in psychiatry: systematic review of current evidence.Translational psychiatry · 2024Pooled it
- Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review.Frontiers in psychiatry · 2024Pooled it
- Trajectories and predictors of response to social cognition training in people with schizophrenia: A proof-of-concept machine learning study.Schizophrenia research · 2024Trial
- Out with AI, in with the psychiatrist: a preference for human-derived clinical decision support in depression care.Translational psychiatry · 2023Trial
- Article
- Data Diversity vs. Model Complexity in the Prediction of Pediatric Bipolar Disorder: Evidence from Academic and Community Clinical Samples.medRxiv : the preprint server for health sciences · 2026Article
- A cautionary tale for AI and machine learning in psychiatry.Translational psychiatry · 2026Review
- Negative and Positive Body-Related Emotions Derived From Voice Recordings During a Mirror Task in Anorexia and Bulimia Nervosa: A Natural Language Processing Approach Using RoBERTa.The International journal of eating disorders · 2026Article
- Better than clairvoyance? Exploratory results on predictors of regular release, substance use and re-delinquency after forensic addiction treatment.Psychiatry, psychology, and law : an interdisciplinary journal of the Australian and New Zealand Association of Psychiatry, Psychology and Law · 2026Article
- Applying Machine Learning to Predict Complex Clinical Course in Youth With Eating Disorders.The International journal of eating disorders · 2026Article
- Concurrent and Prospective Prediction of Suicidal Ideation in Adolescents Using Multimethod Data and Machine Learning: A Pilot Study.JAACAP open · 2025Article
- Predicting Remission in Schizophrenia Using Machine Learning-Assessing the Impact of Sample Size and Predictor Overinclusion.Acta psychiatrica Scandinavica · 2025Article
- Improved Estimation of Correlation Accuracy for Machine Learning Brain-Phenotype Associations.bioRxiv : the preprint server for biology · 2025Article
- Development and Validation of a Brain Aging Biomarker in Middle-Aged and Older Adults: Deep Learning Approach.JMIR aging · 2025Article
- Body fluid biomarkers and psychosis risk in The Accelerating Medicines Partnership® Schizophrenia Program: design considerations.Schizophrenia (Heidelberg, Germany) · 2025Article
- From Preliminary Urinalysis to Decision Support: Machine Learning for UTI Prediction in Real-World Laboratory Data.Journal of personalized medicine · 2025Article
- Article
- A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity.Scientific reports · 2025Article
- Comparison of rTMS and dTMS in TRD: evidence gaps, cost-effectiveness, and ethical priorities.Frontiers in psychiatry · 2025Article
- Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning methods hold promise for personalized care in psychiatry, demonstrating the potential to tailor treatment decisions and stratify patients into clinically meaningful taxonomies. Subsequently, publication counts applying machine learning methods have risen, with different data modalities, mathematically distinct models, and samples of varying size being used to train and test models with the promise of clinical translation. Consequently, and in part due to the preliminary nature of such works, many studies have reported largely varying degrees of accuracy, raising concerns over systematic overestimation and methodological inconsistencies. Furthermore, a lack of procedural evaluation guidelines for non-expert medical professionals and funding bodies leaves many in the field with no means to systematically evaluate the claims, maturity, and clinical readiness of a project. Given the potential of machine learning methods to transform patient care, albeit, contingent on the rigor of employed methods and their dissemination, we deem it necessary to provide a review of current methods, recommendations, and future directions for applied machine learning in psychiatry. In this review we will cover issues of best practice for model training and evaluation, sources of systematic error and overestimation, model explainability vs. trust, the clinical implementation of AI systems, and finally, future directions for our field.
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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.