ArticleInternational journal of population data science2021
Machine learning for identification of frailty in Canadian primary care practices.
Article in International journal of population data science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis.BMC geriatrics · 2023Pooled it
- An explainable online frailty prediction model for community-dwelling older adults based on machine learning algorithms: a cross-sectional study based on retrospective health data.Annals of medicine · 2026Article
- Use of Conventional Artificial Intelligence Methods in the Identification of Frailty: A Scoping Review.Journal of the American Geriatrics Society · 2026Article
- Article
- Explainable Machine Learning Approaches Predict Frailty and Adverse Outcomes in Older Adults: Development and Validation with Two Longitudinal Cohorts.Journal of clinical medicine · 2026Article
- Identifying sex-specific predictors of frailty in Korean community-dwelling older adults using interpretable machine learning.BMC geriatrics · 2026Article
- Use of machine learning for chronic disease case identification in primary care settings: A scoping review of methods, validation, and clinical relevance.The Journal of international medical research · 2026Article
- Article
- Machine Learning in Primary Health Care: The Research Landscape.Healthcare (Basel, Switzerland) · 2025Review
- Capturing factors associated with frailty using routinely collected electronic medical record data in British Columbia, Canada, primary care settings.Primary health care research & development · 2025Article
- Frailty identification using a sensor-based upper-extremity function test: a deep learning approach.Scientific reports · 2025Article
- Development and indirect validation of a model predicting frailty in the French healthcare claims database.Scientific reports · 2025Article
- Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study.JMIR aging · 2025Article
- AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- Artificial Intelligence (AI)-Driven Frailty Prediction Using Electronic Health Records in Hospitalized Patients With Cardiovascular Disease.Circulation reports · 2024Article
- The application of machine learning for identifying frailty in older patients during hospital admission.BMC medical informatics and decision making · 2024Article
- Article
- A machine learning-based assistant tool for early frailty screening of patients receiving maintenance hemodialysis.International urology and nephrology · 2024Article
- Use of Artificial Intelligence in the Identification and Management of Frailty: A Scoping Review Protocol.BMJ open · 2023Article
- Machine Learning Models to Predict Future Frailty in Community-Dwelling Middle-Aged and Older Adults: The ELSA Cohort Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionFrailty is a medical syndrome, commonly affecting people aged 65 years and over and is characterized by a greater risk of adverse outcomes following illness or injury. Electronic medical records contain a large amount of longitudinal data that can be used for primary care research. Machine learning can fully utilize this wide breadth of data for the detection of diseases and syndromes. The creation of a frailty case definition using machine learning may facilitate early intervention, inform advanced screening tests, and allow for surveillance.
objectivesThe objective of this study was to develop a validated case definition of frailty for the primary care context, using machine learning.
methodsPhysicians participating in the Canadian Primary Care Sentinel Surveillance Network across Canada were asked to retrospectively identify the level of frailty present in a sample of their own patients (total n
resultsThe prevalence of frailty within our sample was 18.4%. Of the eight models developed to identify frail patients, an XGBoost model achieved the highest sensitivity (78.14%) and specificity (74.41%). The balanced training dataset did not improve classification performance. Sensitivity analyses did not show improved performance for cut-offs other than 5.
conclusionSupervised machine learning was able to create well performing classification models for frailty. Future research is needed to assess frailty inter-rater reliability, and link multiple data sources for frailty identification.
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