Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions

TL;DR

This paper introduces a novel method that integrates medical knowledge graphs with ICU time-series data to improve predictive accuracy and interpretability, especially in cases of missing data.

Contribution

It is the first to incorporate structured medical knowledge via knowledge graphs into ICU predictive models, enhancing performance and interpretability.

Findings

Improved prediction accuracy with knowledge graph integration

Enhanced model robustness with missing data scenarios

Added interpretability through knowledge graph node analysis

Abstract

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.