Scholarlink

Background <p>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.</p> Objective <p>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.</p> Materials and methods <p>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. </p> Results <p>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.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月25日

查看全文
  • Hongsoo Kim,
  • Jimin Yoon,
  • Hyerim Noh,
  • Sunghun Yun,
  • Seungyeon Chun,
  • Woojoo Lee

摘要

Background

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.

Objective

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.

Materials and methods

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.

Results

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.

Conclusion

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.