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FT-Transformer DeepSurv: A Novel Approach to Predicting Post-Discharge Mortality in Older Adults
Jae Yeon Chung1, Sung Hwan Ji1, Jun Sang Yoo1
1Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.
Studies in Health Technology and Informatics
|July 3, 2026
Abstract:
No validated tools exist to predict mortality at discharge for older ED patients (≥65), limiting Advance Directive (AD) engagement. Using 41,612 EHR records from Samsung Medical Center (2018-2022), our FT-Transformer DeepSurv model achieved a test C-Index of 0.8747 with the smallest generalization gap (0.015) among four compared models, identifying inflammation and comorbidity burden as dominant predictors over age. This explainable model supports safe discharge planning and proactive AD discussions for high-risk elderly patients.
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