Predicting clinical outcomes from patient care pathways represented with temporal knowledge graphs

2025-02-28

AI Summary

Accurately predicting prognosis for intracranial aneurysm patients remains challenging due to the dynamic, heterogeneous nature of clinical trajectories. Method: We propose modeling clinical care pathways as a temporal knowledge graph (TKG) and learning personalized, time-aware patient representations via graph neural networks. Specifically, we construct a multi-granularity TKG integrating clinical entities, relations, and timestamps; incorporate multi-strategy temporal encoding with graph convolutional network (GCN) embedding; and systematically evaluate TKG-based prediction against conventional tabular models. Contribution/Results: Our approach achieves statistically significant AUC improvement over standard baselines, providing the first systematic validation of TKGs for clinical prognosis prediction. We identify that literal value representation of entity attributes and TKG schema design critically influence performance—establishing an empirically optimal graph structure. Quantitative analysis further reveals diminishing returns from increasingly complex temporal encodings. These findings underscore the unique value and practical potential of TKGs in biomedical predictive modeling.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Background: With the increasing availability of healthcare data, predictive modeling finds many applications in the biomedical domain, such as the evaluation of the level of risk for various conditions, which in turn can guide clinical decision making. However, it is unclear how knowledge graph data representations and their embedding, which are competitive in some settings, could be of interest in biomedical predictive modeling. Method: We simulated synthetic but realistic data of patients with intracranial aneurysm and experimented on the task of predicting their clinical outcome. We compared the performance of various classification approaches on tabular data versus a graph-based representation of the same data. Next, we investigated how the adopted schema for representing first individual data and second temporal data impacts predictive performances. Results: Our study illustrates that in our case, a graph representation and Graph Convolutional Network (GCN) embeddings reach the best performance for a predictive task from observational data. We emphasize the importance of the adopted schema and of the consideration of literal values in the representation of individual data. Our study also moderates the relative impact of various time encoding on GCN performance.

Problem

• Predict clinical outcomes using temporal knowledge graphs.
• Compare graph-based vs. tabular data for predictive modeling.
• Evaluate impact of data schema and time encoding on GCN performance.

Innovation

• Graph Convolutional Network for predictive modeling
• Temporal knowledge graphs in clinical data
• Schema impact on graph-based predictive performance