Intensive Care Units (ICU) require comprehensive patient data integration for enhanced clinical outcome predictions, crucial for assessing patient conditions. Recent deep learning advances have utilized patient time series data, and fusion models have incorporated unstructured clinical reports, improving predictive performance. However, integrating established medical knowledge into these models has not yet been explored. The medical domain's data, rich in structural relationships, can be harnessed through knowledge graphs derived from clinical ontologies like the Unified Medical Language System (UMLS) for better predictions. Our proposed methodology integrates this knowledge with ICU data, improving clinical decision modeling. It combines graph representations with vital signs and clinical reports, enhancing performance, especially when data is missing. Additionally, our model includes an interpretability component to understand how knowledge graph nodes affect predictions.

翻译：重症监护病房(ICU)需要整合全面的患者数据以提升临床结局预测能力，这对评估患者病情至关重要。近期深度学习进展已利用患者时间序列数据，且融合模型纳入了非结构化临床报告，改善了预测性能。然而，将既定医学知识整合至这些模型的研究尚属空白。医学领域数据富含结构关系，可通过从临床本体（如统一医学语言系统UMLS）派生的知识图谱加以利用，从而实现更优预测。我们提出的方法将该知识与ICU数据融合，改进了临床决策建模。该方法将图表示与生命体征及临床报告相结合，在数据缺失时仍能提升性能。此外，模型还包含可解释性组件，以理解知识图谱节点如何影响预测结果。