Using natural language processing on electronic health records to enhance detection and prediction of psychosis risk.
Open accessAbstract
BACKGROUND: Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis. METHOD: The 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. RESULTS: A 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. CONCLUSIONS: Using 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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Text is important in clinical prediction modeling within psychiatry.
“Indeed, models trained with both structured and text-based predictors performed practically equivalent to models trained with only text-based features; this underscores the importance of text in clinical prediction modeling within psychiatry.12,31”
From Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning (2025) · cites this paper as [12]
Harrell C and AUROC are equivalent in binary outcomes, but direct comparisons cannot be made with censored data such as those used by Irving and colleagues.
“Harrell C and AUROC are equivalent in binary outcomes, but direct comparisons cannot be made with censored data such as those used by Irving and colleagues.12”
From Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning (2025) · cites this paper as [12]
Performance from our models predicting schizophrenia is in line with the literature, for example with Irving et al, which achieved a Harrell C of 0.79 to 0.86 for 10-year survival prediction of onset of psychosis from an index date.
“Performance from our models predicting schizophrenia is in line with the literature, for example with Irving et al,12 which achieved a Harrell C of 0.79 to 0.86 for 10-year survival prediction of onset of psychosis from an index date.”
From Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning (2025) · cites this paper as [12]
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- Authors
- Irving, J., Patel, R., Oliver, D., Colling, C., Pritchard, M., Broadbent, M., Baldwin, H., Stahl, D., Stewart, R., & Fusar-Poli, P.
- Affiliations
- King's College London (GB)
- South London and Maudsley NHS Foundation Trust (GB)
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- ORCID
- Venue
- Schizophrenia Bulletin
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-10-07
- Volume
- 47
- Issue
- 2
- Pages
- 405-414
- ISSN
- 0586-7614 (ISSN-L)
- 1745-1701
- PMC
- PMC7965059
- OpenAlex
- W3091781426
- Cited by
- 92
- References
- 55
- Citations by year
- 2026: 19 · 2025: 20 · 2024: 19 · 2023: 11 · 2022: 16 · 2021: 6 · 2020: 1
- FWCI
- 6.20
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Schizophrenia Bulletin (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Health Data Research UK Fellowship (MR/S003118/1)
- Starter Grant for Clinical Lecturers (SGL015/1020)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- psychosis prediction
- MeSH terms
- Adolescent, Adult, Female, Follow-Up Studies, Humans, London, Male, Middle Aged, Natural Language Processing, Psychotic Disorders, Retrospective Studies, Models, Statistical, Risk Assessment, Young Adult, Electronic Health Records
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