Predicting clinical outcomes from patient care pathways represented with temporal knowledge graphs
Journal:
arXiv
Published Date:
Feb 28, 2025
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.
