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This study aims to develop explainable machine learning methods to predict risks of institutionalization and mortality among older home care recipients.

June 27, 2026BMC Medical Informatics and Decision Making0 citationsOpen Access

Machine learning for predicting institutionalization and mortality risks among older home care recipients with routinely collected need assessment data: explainable AI for long-term care

HKHongsoo Kim JYJimin Yoon HNHyerim Noh

Key Points

This study aims to develop explainable machine learning methods to predict risks of institutionalization and mortality among older home care recipients.
Evaluated ML algorithms using standardized functional assessment data from 36,526 older beneficiaries in a nationwide LTCI program.
Applied multinomial logistic regression, LASSO, XGBoost, and random forest algorithms to classify individuals by institutionalization or death.
Assessed model performance using metrics such as AUC, accuracy, sensitivity, and specificity.
10.4% of newly enrolled home care recipients were institutionalized within 6 months, increasing to 18.8% within 12 months.
LASSO model identified advanced dementia symptoms, need for suctioning, and dementia diagnosis as key predictors of institutionalization.
Mortality predictors included oxygen therapy needs, cancer pain management, and diabetic foot care.

Abstract

Identifying older home care recipients at risk of institutionalization in advance is crucial for providing preventive services. Supporting the decision-making of care workers in prioritizing their limited resources would be useful for promoting an aging-in-place policy. This study aims to develop and interpret explainable machine learning (ML) methods for predicting institutionalization and mortality as competing events among older adults newly entering the Long-Term Care Insurance (LTCI) home care system. We explored and evaluated ML algorithms using routinely collected standardized functional assessment data to examine the care needs of 36,526 older beneficiaries enrolled in a nationwide public LTCI program. Individuals were classified according to whether institutionalization or death occurred first during follow-up. Accordingly, we employed multinomial logistic regression (MLR), least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), and random forest (RF) algorithms to predict institutionalization and death in a multinomial approach. The performance of the model was assessed via the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and expected calibration error (ECE). Youden’s J statistic was used to determine the cutoff point. Among newly enrolled home care beneficiaries, 10.4% were institutionalized in long-term care facilities within six months, increasing to 18.8% within 12 months after the initiation of long-term care services. The LASSO model achieved the best performance, with the most predictive features of institutionalization including eating nonfood items which marked the advanced dementia, need for suctioning (aspiration care) and dementia diagnosis. The most predictive features of mortality included need for oxygen therapy, cancer pain management and diabetic foot care. The newly developed algorithm may be implemented to examine the care needs of older home care users without additional risk assessment and for early intervention to reduce institutionalization risks.

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