Machine Learning-Based Prediction of Planned Home Care Follow-Up Intervals in Older Adults: Evidence From the YASAM Observational Cohort
Journal of the American Medical Directors Association, cilt.27, sa.9, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 27 Sayı: 9
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.jamda.2026.106381
- Dergi Adı: Journal of the American Medical Directors Association
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Abstracts in Social Gerontology, CINAHL, EMBASE, MEDLINE, Academic Search Ultimate (EBSCO)
- Anahtar Kelimeler: Artificial intelligence, geriatric assessment, healthy aging, home care, machine learning, older adults
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
Objectives To develop and validate machine learning models for predicting clinician-determined follow-up interval categories in home care services among older adults enrolled in the YASAM (Healthy Aging Team Supported Home Care Services) program. Design Secondary analysis of a multicenter observational cohort study. Setting and Participants A total of 4783 community-dwelling adults aged ≥80 years enrolled in the YASAM program between 2023 and 2024. Methods The primary outcome was follow-up frequency time, defined as the planned interval between consecutive face-to-face home care visits determined by an interdisciplinary geriatric care team following comprehensive geriatric assessment and used as the reference standard for model development. Follow-up frequency time was categorized into short (≤30 days), medium (31-90 days), and long (>90 days) follow-up intervals. Seventeen candidate predictors representing demographic, clinical, cognitive, functional, nutritional, mood-related, frailty, and physical performance domains were evaluated. After correlation-based feature selection, 8 core predictors were retained. Missing data were handled using feature-wise imputation. Random forest, extreme gradient boosting, and feed-forward artificial neural network models were developed using a 70/15/15 training-validation-test split. Model performance was assessed using accuracy, precision, recall, F1 score, and confusion matrices. Results Extreme gradient boosting demonstrated the highest classification performance, achieving an accuracy of 83%, a precision of 0.83, a recall of 0.82, and an F1 score of 0.82. Random forest achieved an accuracy of 81%, whereas the neural network achieved an accuracy of 78%. Misclassifications occurred predominantly between adjacent follow-up interval categories. SHapley Additive exPlanation–based analyses identified physical performance, cognitive function, nutritional status, and frailty-related measures as major contributors to follow-up interval prediction. Conclusions and Implications Machine learning models demonstrated substantial predictive performance with interdisciplinary team–determined follow-up interval categories among community-dwelling older adults. Explainable artificial intelligence analyses highlighted the contribution of multidimensional geriatric assessment domains to individualized follow-up planning. These findings support the potential role of artificial intelligence–assisted decision support in home-care scheduling, although external validation is required before routine clinical implementation.