ArticleJournal of clinical and translational science2023
Identification of delirium from real-world electronic health record clinical notes.
Article in Journal of clinical and translational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Natural language processing for geriatric syndromes: a systematic review of methods, applications, and challenges.BMC medical informatics and decision making · 2026Pooled it
- The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review.Age and ageing · 2024Pooled it
- A multi-site benchmarking framework for scalable extraction of geriatric care constructs from electronic health records.npj health systems · 2026Article
- 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
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Natural language processing techniques to detect delirium in hospitalized patients from clinical notes: a systematic review.NPJ digital medicine · 2025Article
- 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
- Validation of a chart review method for identifying delirium in the emergency department.The American journal of emergency medicine · 2025Observational
- Machine Learning Multimodal Model for Delirium Risk Stratification.JAMA network open · 2025Article
- Development and Validation of Natural Language Processing Algorithms in the ENACT National Electronic Health Record Research Network.medRxiv : the preprint server for health sciences · 2025Article
- Characteristics Associated with Length of Stay in Intensive Care Unit for Cancer Patients in China: A Nationwide Retrospective Cohort Study.Journal of multidisciplinary healthcare · 2025Article
- Development and validation of natural language processing algorithms in the national ENACT network.Journal of clinical and translational science · 2025Article
- Advancing regulatory science through real-world data and real-world evidence.Journal of clinical and translational science · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Introduction: We tested the ability of our natural language processing (NLP) algorithm to identify delirium episodes in a large-scale study using real-world clinical notes. Methods: We used the Rochester Epidemiology Project to identify persons ≥ 65 years who were hospitalized between 2011 and 2017. We identified all persons with an International Classification of Diseases code for delirium within ±14 days of a hospitalization. We independently applied our NLP algorithm to all clinical notes for this same population. We calculated rates using number of delirium episodes as the numerator and number of hospitalizations as the denominator. Rates were estimated overall, by demographic characteristics, and by year of episode, and differences were tested using Poisson regression. Results: In total, 14,255 persons had 37,554 hospitalizations between 2011 and 2017. The code-based delirium rate was 3.02 per 100 hospitalizations (95% CI: 2.85, 3.20). The NLP-based rate was 7.36 per 100 (95% CI: 7.09, 7.64). Rates increased with age (both Conclusions: The NLP algorithm identified more delirium episodes compared to the ICD code method. However, NLP may still underestimate delirium cases because of limitations in real-world clinical notes, including incomplete documentation, practice changes over time, and missing clinical notes in some time periods.
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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.