Abstract
Accurate and interpretable mortality risk prediction in intensive care units (ICUs) remains a critical challenge due to the irregular temporal structure of electronic health records (EHRs), the complexity of longitudinal disease trajectories, and the lack of clinically grounded explanations in many data-driven models. To address these challenges, we propose \textit{TA-RNN-Medical-Hybrid}, a time-aware and knowledge-enriched deep learning framework that jointly models longitudinal clinical sequences and irregular temporal dynamics through explicit continuous-time encoding, along with standardized medical concept representations. The proposed framework extends time-aware recurrent modeling by integrating explicit continuous-time embeddings that operate independently of visit indexing, SNOMED-based disease representations, and a hierarchical dual-level attention mechanism that captures both visit-level temporal importance and feature/concept-level clinical relevance. This design enables accurate mortality risk estimation while providing transparent and clinically meaningful explanations aligned with established medical knowledge. We evaluate the proposed approach on the MIMIC-III critical care dataset and compare it against strong time-aware and sequential baselines. Experimental results demonstrate that TA-RNN-Medical-Hybrid consistently improves predictive performance in terms of AUC, accuracy, and recall-oriented F$_2$-score. Moreover, qualitative analysis shows that the model effectively decomposes mortality risk across time and clinical concepts, yielding interpretable insights into disease severity, chronicity, and temporal progression. Overall, the proposed framework bridges the gap between predictive accuracy and clinical interpretability, offering a scalable and transparent solution for high-stakes ICU decision support systems.
Abstract (translated)
重症监护病房（ICUs）中准确且可解释的死亡风险预测仍然是一个关键挑战，原因在于电子健康记录（EHRs）的不规则时间结构、纵向疾病轨迹的复杂性以及许多基于数据驱动模型缺乏临床基础解释。为了解决这些挑战，我们提出了**TA-RNN-Medical-Hybrid**，这是一种时间感知且知识丰富的深度学习框架，它通过显式的连续时间编码同时建模纵向临床序列和不规则的时间动态，并使用标准化的医疗概念表示。该框架通过整合独立于访问索引操作的明确连续时间嵌入、基于SNOMED的疾病表示以及一个分层双层级注意力机制（能够捕捉到就诊级别的时间重要性和特征/概念级别的临床相关性）扩展了时间感知递归建模方法。这种设计不仅实现了准确的风险评估，还提供了与已建立的医学知识一致的透明且具有临床意义的解释。 我们使用MIMIC-III重症监护数据集对所提出的方法进行了评估，并将其与强大的时间感知和顺序基线模型进行了比较。实验结果表明，在AUC、精度和召回导向F$_2$得分方面，TA-RNN-Medical-Hybrid在预测性能上持续改进。此外，定性分析显示该模型能够有效分解死亡风险的时间分布和临床概念，从而为疾病的严重程度、慢性病程以及时间进展提供了可解释的见解。 总的来说，所提出的框架填补了预测准确性和临床解释之间的空白，并为高风险ICU决策支持系统提供了一种具有扩展性和透明度的解决方案。
URL
https://arxiv.org/abs/2603.08278
