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Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method.

Kyoung Hee Cho¹, Jong-Min Paek², Kwang-Man Ko²

¹Department of Health Policy and Management, SangJi University, Wonju-si 26339, Republic of Korea.

Geriatrics (Basel, Switzerland)

|October 27, 2023

Summary

This study developed a deep learning model to predict survival in older adults, identifying key risk factors like comorbidity and frailty for better health management and longevity.

Keywords:

community-dwelling older individuals comorbidity deep learning frailty survival prediction model

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Area of Science:

Gerontology
Artificial Intelligence in Healthcare
Public Health

Background:

Aging populations necessitate strategies for healthy aging and economic participation.
Predicting survival and identifying health risks are crucial for older adults.

Purpose of the Study:

To develop a deep learning model for predicting survival times in community-dwelling older individuals.
To identify and quantify the influence of various risk factors on survival periods.

Main Methods:

Utilized Korean National Health Insurance Service claims data for 189,697 individuals aged 66 years over 11 years (2009-2019).
Developed and validated a deep learning-based survival time prediction model (C-statistic = 0.7011).

Main Results:

Identified significant survival predictors: Charlson's comorbidity index, frailty index, long-term care grade, disability grade, income, combined diabetes/hypertension/dyslipidemia, sex, smoking, and alcohol consumption.
Charlson's comorbidity index and frailty index demonstrated the strongest predictive power (SHAP values: 0.0445 and 0.0443, respectively).

Conclusions:

Deep learning models can effectively predict survival in older adults.
Identified modifiable risk factors (e.g., comorbidities, frailty) that can inform personalized health management strategies for longevity.