ArticleJournal of the American Medical Informatics Association : JAMIA2021
Development and validation of a prediction model for actionable aspects of frailty in the text of clinicians' encounter notes.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 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 in detecting and predicting falls within the healthcare setting: a systematic review.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2023Pooled it
- Geriatric syndromes extraction from discharge summaries: a new dataset, annotation scheme and initial findings.Frontiers in digital health · 2026Article
- Association between frailty and frailty change with chronic lung disease: results from two prospective cohort studies.BMC public health · 2025Article
- Measuring Goal-Concordant Care Using Electronic Clinical Notes.JAMA network open · 2025Article
- Improving Surgical Outcomes for Older Adults with Adoption of Technological Advances in Comprehensive Geriatric Assessment.Seminars in colon & rectal surgery · 2024Article
- Enhancing Frailty Assessments for Transcatheter Aortic Valve Replacement Patients Using Structured and Unstructured Data: Real-World Evidence Study.JMIR aging · 2024Article
- Leveraging the electronic health record to identify delivery of goal-concordant care.medRxiv : the preprint server for health sciences · 2024Article
- Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review.Journal of medical Internet research · 2023Article
- Development of a Machine Learning Model to Identify Colorectal Cancer Stage in Medicare Claims.JCO clinical cancer informatics · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
objectiveFrailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sources. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes. MATERIALS AND
methodsWe used an active learning strategy to select notes from the EHR and annotated each sentence for 4 actionable aspects of frailty: respiratory impairment, musculoskeletal problems, fall risk, and nutritional deficiencies. We compared the performance of regression, tree-based, and neural network models to predict the labels for each sentence. We evaluated performance with the scaled Brier score (SBS), where 1 is perfect and 0 is uninformative, and the positive predictive value (PPV).
resultsWe manually annotated 155 952 sentences from 326 patients. Elastic net regression had the best performance across all 4 frailty aspects (SBS 0.52, 95% confidence interval [CI] 0.49-0.54) followed by random forests (SBS 0.49, 95% CI 0.47-0.51), and multi-task neural networks (SBS 0.39, 95% CI 0.37-0.42). For the elastic net model, the PPV for identifying the presence of respiratory impairment was 54.8% (95% CI 53.3%-56.6%) at a sensitivity of 80%. DISCUSSION: Classification models using EHR notes can effectively identify actionable aspects of frailty among patients living with chronic lung disease. Regression performed better than random forest and neural network models.
conclusionsNLP-based models offer promising support to population health management programs that seek to identify and refer community-dwelling patients with frailty for evidence-based interventions.
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