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。

DySurv: ICU 生存预测的动态深度学习模型