ReviewThe Lancet. Digital health2022
The promise of a model-based psychiatry: building computational models of mental ill health.
Review in The Lancet. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 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
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Neuroimaging Insights into the Public Health Burden of Neuropsychiatric Disorders: A Systematic Review of Electroencephalography-Based Cognitive Biomarkers.Medicina (Kaunas, Lithuania) · 2025Pooled it
- The Application of AI to Ecological Momentary Assessment Data in Suicide Research: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Relevance of the negative valence system, the cognitive system, and their interactions for understanding aggression in mental disorders.Der Nervenarzt · 2026Review
- Rapid and reliable computational markers of decision-making for predicting daily smoking behavior and smoking cessation treatment outcomes.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- cpm: A python library for theory-driven modelling in computational psychiatry.PLoS computational biology · 2026Article
- A clinically actionable framework for personalizing iCBT to improve depression outcomes.NPJ digital medicine · 2026Review
- Widely used bandit tasks elicit diverging belief updating phenotypes in healthy adults.Research square · 2026Article
- Cognitive modeling of real-world behavior for understanding mental health.Trends in cognitive sciences · 2026Review
- Deep learning characterizes depression and suicidal ideation in young adults from eye movements.NPJ digital medicine · 2026Article
- Clinical psychopathology-based early relapse prediction model using speech and language in psychosis.Schizophrenia research. Cognition · 2026Review
- Threading the Needle: Practical Considerations for Merging Theory-Driven Computational Psychiatry With Data-Driven Analytics to Enhance Precision Health at Scale.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Modeling metacognition and executive functions in the metacognitive wisconsin card sorting test using the neuropsychological digital-twin method.Scientific reports · 2026Article
- Computational whole-body-exposome models for global precision brain health.Nature communications · 2025Article
- How public involvement can improve the science of AI.Proceedings of the National Academy of Sciences of the United States of America · 2025Review
- Ethical aspects of the application of artificial intelligence in psychiatry.Postepy psychiatrii neurologii · 2025Review
- Large-scale experimental investigation of the reliability of confidence measures.Communications psychology · 2025Article
- Misclassification in memory modification ineLife · 2025Article
- A comparison of the quality of integrated case formulations produced by UK psychiatric trainees and an artificial intelligence-assisted application.BJPsych bulletin · 2025Article
- Beyond discrete classifications: a computational approach to the continuum of cognition and behavior in children.Npj mental health research · 2025Article
- Practical AI application in psychiatry: historical review and future directions.Molecular psychiatry · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.
Indexed as
Identifiers
What Socratic 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 Socratic graph.