ArticleSchizophrenia bulletin2021
Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk.
Article in Schizophrenia bulletin, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 55 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it, 86 citations in OpenAlex.
- Prognostic accuracy and clinical utility of psychometric instruments for individuals at clinical high-risk of psychosis: a systematic review and meta-analysis.Molecular psychiatry · 2022Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Psychosis Polyrisk Score and Polygenic Risk Score to Improve Detection and Prognosis in Individuals at Clinical High Risk for Psychosis: Model Development and Internal Validation.Schizophrenia bulletin · 2026Article
- Measuring Depression Severity With Clinical Global Impression-Severity Scale Scores From Clinical Notes Using Large Language Models: Validation Study.JMIR formative research · 2026Article
- Cross-linguistic benchmarking of NLP metrics for psychosis research.NPJ digital medicine · 2026Article
- Health databases and early identification of autism spectrum disorder: a scoping review.BMJ open · 2026Article
- An integrative NLP framework identifies multilevel linguistic phenotypes of schizophrenia across tasks.Psychological medicine · 2026Article
- Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.JMIR mental health · 2026Observational
- Large language model approach to uncover reasoning patterns in forensic psychiatric assessment.Scientific reports · 2026Article
- Characterisation of serious mental illness trajectories through transdiagnostic clinical features.The British journal of psychiatry : the journal of mental science · 2026Article
- Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health.Pharmacopsychiatry · 2026Review
- Development and validation of a precision treatment rules for first-line antipsychotic recommendations in first episode psychosis jointly incorporating effectiveness, side effects and patient preferences.Translational psychiatry · 2026Article
- Scalable depression monitoring with smartphone speech using a multimodal benchmark and topic analysis.NPJ digital medicine · 2026Article
- Quantifying improvement of psychotic symptoms in clozapine-treated schizophrenia: clinical note analysis with large language models.Scientific reports · 2026Article
- Development and validation of a machine learning model to identify individuals at high risk for psychotic disorders using medical record data.BMC psychiatry · 2026Article
- Identifying psychosis episodes in psychiatric admission notes via rule-based methods, machine learning, and pre-trained language models.Translational psychiatry · 2025Article
- Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing.Journal of neurological surgery. Part B, Skull base · 2025Article
- A highly scalable deep learning language model for common risks prediction among psychiatric inpatients.BMC medicine · 2025Article
- Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning.JAMA psychiatry · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundUsing novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis.
methodThe study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell's C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors: tearfulness, poor appetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia.
resultsA total of 92 151 patients with a first index diagnosis of nonorganic and nonpsychotic mental disorder within the SLaM Trust were included in the derivation (n = 28 297) or external validation (n = 63 854) data sets. Mean age was 33.6 years, 50.7% were women, and 67.0% were of white race/ethnicity. Mean follow-up was 1590 days. The overall 6-year risk of psychosis in secondary mental health care was 3.4 (95% CI, 3.3-3.6). External validation indicated strong performance on unseen data (Harrell's C 0.85, 95% CI 0.84-0.86), an increase of 0.06 from the original model.
conclusionsUsing NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.
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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.