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.

翻译：在重症监护病房（ICU）中，由于电子健康记录（EHR）具有不规则的时间结构、纵向疾病轨迹的复杂性，以及许多数据驱动模型缺乏临床依据的解释，实现准确且可解释的死亡风险预测仍然是一项关键挑战。为应对这些挑战，我们提出了 \textit{TA-RNN-Medical-Hybrid}，一种时间感知且知识增强的深度学习框架。该框架通过显式的连续时间编码，结合标准化的医学概念表示，共同建模纵向临床序列与不规则的时间动态。所提出的框架扩展了时间感知循环建模，集成了独立于就诊索引的显式连续时间嵌入、基于SNOMED的疾病表示，以及一个分层的双级注意力机制，该机制同时捕捉就诊级别的时间重要性和特征/概念级别的临床相关性。这种设计能够在实现准确死亡风险估计的同时，提供透明且具有临床意义的解释，并与既有的医学知识保持一致。我们在MIMIC-III重症监护数据集上评估了所提出的方法，并与强时间感知和序列基线模型进行了比较。实验结果表明，TA-RNN-Medical-Hybrid 在AUC、准确率和面向召回率的F$_2$-分数方面持续提升了预测性能。此外，定性分析表明，该模型能有效分解跨时间和临床概念的死亡风险，从而对疾病严重程度、慢性程度及时间进展产生可解释的洞见。总体而言，所提出的框架弥合了预测准确性与临床可解释性之间的差距，为高风险ICU决策支持系统提供了一种可扩展且透明的解决方案。