深度学习模型统一处理动态数据与缺失值，提升重症监护预后预测精度。
Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation
- 融合函数型数据表示与梯度缺失值填补，端到端建模风险事件
- 在模拟数据和MIMIC-IV等真实数据上准确率显著优于传统模型
- 适合需要处理不规则、不完整临床时间序列的重症医疗场景
我们提出功能竞争风险网络（FCRN），一种用于离散时间竞争风险生存分析的统一深度学习框架，可无缝整合函数型协变量并处理缺失数据。通过结合微网络基底层对函数数据建模与基于梯度的插补模块，FCRN能同时学习缺失值填补与特定事件风险预测。在多个模拟数据集及基于MIMIC-IV和克利夫兰诊所数据的真实重症监护案例研究中，其预测准确率显著优于随机生存森林与传统竞争风险模型。该方法通过更有效地捕捉动态风险因素与静态预测因子，提升了危重症预后建模能力，尤其适用于不规则且不完整的数据。
原文摘要 · Abstract (English)
We introduce the Functional Competing Risk Net (FCRN), a unified deep-learning framework for discrete-time survival analysis under competing risks, which seamlessly integrates functional covariates and handles missing data within an end-to-end model. By combining a micro-network Basis Layer for functional data representation with a gradient-based imputation module, FCRN simultaneously learns to impute missing values and predict event-specific hazards. Evaluated on multiple simulated datasets and a real-world ICU case study using the MIMIC-IV and Cleveland Clinic datasets, FCRN demonstrates substantial improvements in prediction accuracy over random survival forests and traditional competing risks models. This approach advances prognostic modeling in critical care by more effectively capturing dynamic risk factors and static predictors while accommodating irregular and incomplete data.
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