ArticleJournal of affective disorders reports2022
Portability of natural language processing methods to detect suicidality from clinical text in US and UK electronic health records.
Article in Journal of affective disorders reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A machine-assisted framework for systematic error analysis in clinical concept extraction.Nature communications · 2026Article
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Detecting Pediatric Emergency Service Use for Suicide and Self-Harm: Multimodal Analysis of 3828 Encounters.JMIR mental health · 2026Article
- Defining suicidality phenotypes for genetic studies: perspectives of the Psychiatric Genomics Consortium Suicide Working Group.Molecular psychiatry · 2025Review
- Advancing delirium detection through the Open Health Natural Language Processing Consortium and the Evolve to Next-Gen Accrual to Clinical Trials Network.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Article
- Evaluating natural language processing derived linguistic features associated with current suicidal ideation, past attempts, and future suicidal behavior.Journal of psychiatric research · 2025Article
- Absence of nonfatal suicidal behavior preceding suicide death reveals differences in clinical risks.Psychiatry research · 2025Article
- Distinguishing clinical and genetic risk factors for suicidal ideation and behavior in a diverse hospital population.Translational psychiatry · 2025Article
- Enhancing Cross-Domain Generalizability in Social Determinants of Health Extraction with Prompt-Tuning Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Review
- Electronic Health Record Phenotyping of Pediatric Suicide-Related Emergency Department Visits.JAMA network open · 2024Article
- A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Absence of nonfatal suicidal behavior preceding suicide death reveals differences in clinical risks.medRxiv : the preprint server for health sciences · 2024Article
- A multisite comparison using electronic health records and natural language processing to identify the association between suicidality and hospital readmission amongst patients with eating disorders.The International journal of eating disorders · 2023Article
- Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records.Frontiers in psychiatry · 2023Article
- Article
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Authors and funding
7 authors.
Funding
Abstract
Background: In the global effort to prevent death by suicide, many academic medical institutions are implementing natural language processing (NLP) approaches to detect suicidality from unstructured clinical text in electronic health records (EHRs), with the hope of targeting timely, preventative interventions to individuals most at risk of suicide. Despite the international need, the development of these NLP approaches in EHRs has been largely local and not shared across healthcare systems. Methods: In this study, we developed a process to share NLP approaches that were individually developed at King's College London (KCL), UK and Weill Cornell Medicine (WCM), US - two academic medical centers based in different countries with vastly different healthcare systems. We tested and compared the algorithms' performance on manually annotated clinical notes (KCL: Results: After a successful technical porting of the NLP approaches, our quantitative evaluation determined that independently developed NLP approaches can detect suicidality at another healthcare organization with a different EHR system, clinical documentation processes, and culture, yet do not achieve the same level of success as at the institution where the NLP algorithm was developed (KCL approach: F1-score 0.85 vs. 0.68, WCM approach: F1-score 0.87 vs. 0.72). Limitations: Independent NLP algorithm development and patient cohort selection at the two institutions comprised direct comparability. Conclusions: Shared use of these NLP approaches is a critical step forward towards improving data-driven algorithms for early suicide risk identification and timely prevention.
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