Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method
In an aging society, maintaining healthy aging, preventing death, and enabling a continuation of economic activities are crucial. This study sought to develop a model for predicting survival times among community-dwelling older individuals using a deep learning method, and to identify the level of i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Geriatrics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2308-3417/8/5/105 |
| _version_ | 1797573802706599936 |
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| author | Kyoung Hee Cho Jong-Min Paek Kwang-Man Ko |
| author_facet | Kyoung Hee Cho Jong-Min Paek Kwang-Man Ko |
| author_sort | Kyoung Hee Cho |
| collection | DOAJ |
| description | In an aging society, maintaining healthy aging, preventing death, and enabling a continuation of economic activities are crucial. This study sought to develop a model for predicting survival times among community-dwelling older individuals using a deep learning method, and to identify the level of influence of various risk factors on the survival period, so that older individuals 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 9462 (5.0%) died. Using deep-learning-based models (C statistics = 0.7011), we identified various factors impacting survival: 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 habits. In particular, Charlson’s comorbidity index (SHAP value: 0.0445) and frailty index (SHAP value: 0.0443) were strong predictors of survival time. Prediction models may help researchers to identify potentially modifiable risk factors that may affect survival. |
| first_indexed | 2024-03-10T21:14:12Z |
| format | Article |
| id | doaj.art-e142f8fdff334a74894c7847fd294900 |
| institution | Directory Open Access Journal |
| issn | 2308-3417 |
| language | English |
| last_indexed | 2024-03-10T21:14:12Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geriatrics |
| spelling | doaj.art-e142f8fdff334a74894c7847fd2949002023-11-19T16:36:55ZengMDPI AGGeriatrics2308-34172023-10-018510510.3390/geriatrics8050105Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning MethodKyoung Hee Cho0Jong-Min Paek1Kwang-Man Ko2Department of Health Policy and Management, SangJi University, Wonju-si 26339, Republic of KoreaDepartment of Computer Engineering, SangJi University, Kwang-Man Ko. 83 Sangjidae-gil, Wonju-si 26339, Republic of KoreaDepartment of Computer Engineering, SangJi University, Kwang-Man Ko. 83 Sangjidae-gil, Wonju-si 26339, Republic of KoreaIn an aging society, maintaining healthy aging, preventing death, and enabling a continuation of economic activities are crucial. This study sought to develop a model for predicting survival times among community-dwelling older individuals using a deep learning method, and to identify the level of influence of various risk factors on the survival period, so that older individuals 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 9462 (5.0%) died. Using deep-learning-based models (C statistics = 0.7011), we identified various factors impacting survival: 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 habits. In particular, Charlson’s comorbidity index (SHAP value: 0.0445) and frailty index (SHAP value: 0.0443) were strong predictors of survival time. Prediction models may help researchers to identify potentially modifiable risk factors that may affect survival.https://www.mdpi.com/2308-3417/8/5/105community-dwelling older individualscomorbiditydeep learningfrailtysurvival prediction model |
| spellingShingle | Kyoung Hee Cho Jong-Min Paek Kwang-Man Ko Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method Geriatrics community-dwelling older individuals comorbidity deep learning frailty survival prediction model |
| title | Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method |
| title_full | Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method |
| title_fullStr | Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method |
| title_full_unstemmed | Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method |
| title_short | Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method |
| title_sort | long term survival prediction model for elderly community members using a deep learning method |
| topic | community-dwelling older individuals comorbidity deep learning frailty survival prediction model |
| url | https://www.mdpi.com/2308-3417/8/5/105 |
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