ArticleJournal of general internal medicine2023
Machine Learning-Based Prediction of Functional Disability: a Cohort Study of Japanese Older Adults in 2013-2019.
Article in Journal of general internal medicine, 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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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Disability risk prediction models in community-dwelling older adults: a systematic review.BMC geriatrics · 2026Pooled it
- Risk prediction models for disability in older adults: a systematic review and critical appraisal.BMC geriatrics · 2024Pooled it
- The promise and peril of AI for disability health equity: A justice-centered narrative review.PLOS digital health · 2026Review
- Functional disability screening in the elderly: a machine learning approach with ELSI-Brazil data.GeroScience · 2026Article
- Article
- Development and external validation of a machine learning model for three-tier disability risk stratification in older adults.Frontiers in public health · 2026Article
- Predicting six-month mortality in adult hemophagocytic lymphohistiocytosis with machine learning: a prognostic approach utilizing laboratory data.Annals of medicine · 2025Article
- Construction of disability risk prediction model for the elderly based on machine learning.Scientific reports · 2025Article
- Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study.JMIR rehabilitation and assistive technologies · 2025Article
- Public libraries and functional disability: A cohort study of Japanese older adults.SSM - population health · 2025Article
- Multimodal machine learning to predict response to ultrasound-guided botulinum and vibration therapy in muscle spasticity a clinical and imaging correlation study.Frontiers in bioengineering and biotechnology · 2025Article
- Social interaction and functional status among community-dwelling older adults in Japan: a longitudinal study during the coronavirus disease 2019 pandemic.Frontiers in public health · 2025Article
- Health Statuses of People in Poverty Receiving Public Assistance in Japan: A Scoping Review.JMA journal · 2024Article
- Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt is important to identify older adults at high risk of functional disability and to take preventive measures for them at an early stage. To our knowledge, there are no studies that predict functional disability among community-dwelling older adults using machine learning algorithms.
objectiveTo construct a model that can predict functional disability over 5 years using basic machine learning algorithms.
designA cohort study with a mean follow-up of 5.4 years.
participantsWe used data from the Japan Gerontological Evaluation Study, which involved 73,262 people aged ≥ 65 years who were not certified as requiring long-term care. The baseline survey was conducted in 2013 in 19 municipalities. MAIN MEASURES: We defined the onset of functional disability as the new certification of needing long-term care that was ascertained by linking participants to public registries of long-term care insurance. All 183 candidate predictors were measured by self-report questionnaires. KEY
resultsDuring the study period, 16,361 (22.3%) participants experienced the onset of functional disability. Among machine learning-based models, ridge regression (C statistic = 0.818) and gradient boosting (0.817) effectively predicted functional disability. In both models, we identified age, self-rated health, variables related to falls and posture stabilization, and diagnoses of Parkinson's disease and dementia as important features. Additionally, the ridge regression model identified the household characteristics such as the number of members, income, and receiving public assistance as important predictors, while the gradient boosting model selected moderate physical activity and driving. Based on the ridge regression model, we developed a simplified risk score for functional disability, and it also indicated good performance at the cut-off of 6/7 points.
conclusionsMachine learning-based models showed effective performance prediction over 5 years. Our findings suggest that measuring and adding the variables identified as important features can improve the prediction of functional disability.
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