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.

通过将医学领域的知识图谱与重症监护病房的数据进行整合，结合重要体征和临床报告，提高临床决策建模的性能，特别是在数据缺失时。同时包括一个可解释性组件，以了解知识图谱节点对预测的影响。