Cho, K.H.; Paek, J.-M.; Ko, K.-M. Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method. Geriatrics2023, 8, 105.
Cho, K.H.; Paek, J.-M.; Ko, K.-M. Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method. Geriatrics 2023, 8, 105.
Cho, K.H.; Paek, J.-M.; Ko, K.-M. Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method. Geriatrics2023, 8, 105.
Cho, K.H.; Paek, J.-M.; Ko, K.-M. Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method. Geriatrics 2023, 8, 105.
Abstract
In a super-aged society, maintaining healthy aging, preventing death, and enabling a continua-tion of economic activities are crucial. This study sought to develop a model for predicting the survival time in community-dwelling older individuals by using a deep learning method, and to identify the level of influence of various risk factors on the survival period, so that older in-dividuals can manage their own health. This study used the Korean National Health Insurance Service claims data. We observed community-dwelling older people, aged 66 years, for 11 years and developed a survival time prediction model. Of the 189,697 individuals enrolled at baseline, 180,235 (95.0%) survived from 2009 to 2019, while 9,462 (5.0%) died. Using deep learning based models (C statistics = 0.7011), we identified Charlson’s comorbidity index; the frailty index; long-term care benefit grade; disability grade; income level; a combination of diabetes mellitus, hypertension, and dyslipidemia; sex; smoking status; and alcohol consumption habit as factors impacting survival. In particular, Charlson’s comorbidity index (SHAP value: 0.0445) and frailty index (SHAP value: 0.0443) were strong predictors of survival time. Older individuals should rec-ognize modifiable risk factors that affect survival period in order to manage their own health.
Keywords
community-dwelling older individuals; comorbidity; deep learning; frailty; survival prediction model
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
