ArticleBMJ open2017
Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project.
Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 3 of them syntheses that pooled it.
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Who cites it
111 citing papers in PubMed, 3 syntheses or guidelines pooled it, 233 citations in OpenAlex.
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.Psychiatric research and clinical practice · 2026Article
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- clickBrick prompt engineering: optimizing large language model performance in clinical psychiatry.Npj mental health research · 2026Article
- Treatment resistant depression in electronic health records: definitions matter.BMC psychiatry · 2026Article
- Deep learning driven clustering of post-traumatic stress disorder (PTSD) profiles in student-athletes: implications for precision and stratified mental health support.BMC psychology · 2026Article
- Machine learning in the analysis of mental health at work: a scoping review.Journal of occupational health · 2026Article
- From observation to evidence: harnessing the mental status examination as a real-world data source in clinical research.Frontiers in health services · 2026Article
- A proxy measure of clinical insight in psychosis: an electronic health records-based validation study.BJPsych open · 2025Article
- Automating the extraction of otology symptoms from clinic letters: a methodological study using natural language processing.BMC medical informatics and decision making · 2025Article
- Mood instability as a transdiagnostic predictor of cannabis use in attention-deficit/hyperactivity disorder and depression: A natural language processing analysis of electronic health records from 13,025 adolescents.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- Remote Consultations in England During COVID-19: Challenges in Data Quality, Linkage, and Research Validity.Online journal of public health informatics · 2025Article
- Hospital and Skilled Nursing Facility Networks: Informal Relationships and Their Role in the Placement of Traditional Medicare Beneficiaries With Serious Mental Illness.Health services research · 2025Article
- Machine learning in psychiatric health records: A gold standard approach to trauma annotation.Translational psychiatry · 2025Article
- Identifying people with potentially undiagnosed dementia with Lewy bodies using natural language processing.npj aging · 2025Article
- Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records.Molecular psychiatry · 2025Article
- Article
- Text mining of outpatient narrative notes to predict the risk of psychiatric hospitalization.Translational psychiatry · 2025Article
51 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesWe sought to use natural language processing to develop a suite of language models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate the secondary use of mental healthcare data in research.
designDevelopment and validation of information extraction applications for ascertaining symptoms of SMI in routine mental health records using the Clinical Record Interactive Search (CRIS) data resource; description of their distribution in a corpus of discharge summaries.
settingElectronic records from a large mental healthcare provider serving a geographic catchment of 1.2 million residents in four boroughs of south London, UK.
participantsThe distribution of derived symptoms was described in 23 128 discharge summaries from 7962 patients who had received an SMI diagnosis, and 13 496 discharge summaries from 7575 patients who had received a non-SMI diagnosis. OUTCOME MEASURES: Fifty SMI symptoms were identified by a team of psychiatrists for extraction based on salience and linguistic consistency in records, broadly categorised under positive, negative, disorganisation, manic and catatonic subgroups. Text models for each symptom were generated using the TextHunter tool and the CRIS database.
resultsWe extracted data for 46 symptoms with a median F1 score of 0.88. Four symptom models performed poorly and were excluded. From the corpus of discharge summaries, it was possible to extract symptomatology in 87% of patients with SMI and 60% of patients with non-SMI diagnosis.
conclusionsThis work demonstrates the possibility of automatically extracting a broad range of SMI symptoms from English text discharge summaries for patients with an SMI diagnosis. Descriptive data also indicated that most symptoms cut across diagnoses, rather than being restricted to particular groups.
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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.