Knowledge Graph Representations to enhance Intensive Care Time-Series
  Predictions
Samyak Jain Manuel Burger Gunnar Rätsch Rita Kuznetsova
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.