ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2022
Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
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The trial behind it
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Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Prediction Models for In-Hospital Delirium Using Routinely Collected Electronic Health Record Data: Systematic Review.JMIR medical informatics · 2026Pooled 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
- Comparing three approaches to delirium identification in older patients presenting to the emergency department.European geriatric medicine · 2026Article
- 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
- Artificial Intelligence-Based Delirium Prediction Model for Post-Cardiac Surgery Patients: A Scoping Review.Journal of advanced nursing · 2026Article
- MedADL: high-throughput information extraction of functional status from electronic health records to advance frailty assessment in older adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Geriatric syndromes extraction from discharge summaries: a new dataset, annotation scheme and initial findings.Frontiers in digital health · 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
- Developing the CAM-BERT: Enhancing delirium screening in hospitalized older adults using natural language processing.Computers in biology and medicine · 2025Article
- Machine Learning Multimodal Model for Delirium Risk Stratification.JAMA network open · 2025Article
- Article
- 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
- Hybrid natural language processing tool for semantic annotation of medical texts in Spanish.BMC bioinformatics · 2025Article
- Development and validation of natural language processing algorithms in the national ENACT network.Journal of clinical and translational science · 2025Article
- Improving Surgical Outcomes for Older Adults with Adoption of Technological Advances in Comprehensive Geriatric Assessment.Seminars in colon & rectal surgery · 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
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundDelirium is underdiagnosed in clinical practice and is not routinely coded for billing. Manual chart review can be used to identify the occurrence of delirium; however, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) has the capability to process raw text in electronic health records (EHRs) and determine the meaning of the information. We developed and validated NLP algorithms to automatically identify the occurrence of delirium from EHRs.
methodsThis study used a randomly selected cohort from the population-based Mayo Clinic Biobank (N = 300, age ≥65). We adopted the standardized evidence-based framework confusion assessment method (CAM) to develop and evaluate NLP algorithms to identify the occurrence of delirium using clinical notes in EHRs. Two NLP algorithms were developed based on CAM criteria: one based on the original CAM (NLP-CAM; delirium vs no delirium) and another based on our modified CAM (NLP-mCAM; definite, possible, and no delirium). The sensitivity, specificity, and accuracy were used for concordance in delirium status between NLP algorithms and manual chart review as the gold standard. The prevalence of delirium cases was examined using International Classification of Diseases, 9th Revision (ICD-9), NLP-CAM, and NLP-mCAM.
resultsNLP-CAM demonstrated a sensitivity, specificity, and accuracy of 0.919, 1.000, and 0.967, respectively. NLP-mCAM demonstrated sensitivity, specificity, and accuracy of 0.827, 0.913, and 0.827, respectively. The prevalence analysis of delirium showed that the NLP-CAM algorithm identified 12 651 (9.4%) delirium patients, the NLP-mCAM algorithm identified 20 611 (15.3%) definite delirium cases, and 10 762 (8.0%) possible cases.
conclusionsNLP algorithms based on the standardized evidence-based CAM framework demonstrated high performance in delineating delirium status in an expeditious and cost-effective manner.
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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.