Survival analysis helps approximate underlying distributions of
time-to-events which in the case of critical care like in the ICU can be a
powerful tool for dynamic mortality risk prediction. Extending beyond the
classical Cox model, deep learning techniques have been leveraged over the last
years relaxing the many constraints of their counterparts from statistical
methods. In this work, we propose a novel conditional variational
autoencoder-based method called DySurv which uses a combination of static and
time-series measurements from patient electronic health records in estimating
risk of death dynamically in the ICU. DySurv has been tested on standard
benchmarks where it outperforms most existing methods including other deep
learning methods and we evaluate it on a real-world patient database from
MIMIC-IV. The predictive capacity of DySurv is consistent and the survival
estimates remain disentangled across different datasets supporting the idea
that dynamic deep learning models based on conditional variational inference in
multi-task cases can be robust models for survival analysis.

基于深度学习和条件变分自编码器的 DySurv 方法能够动态地利用患者电子健康记录中的静态和时间序列测量，准确地估计 ICU 中的死亡风险，并在标准基准测试中胜过大多数现有方法，表现一致且可靠。