GraphCHASUR: A Temporal Network Framework for Characterizing Adverse Event Dynamics

Music City Center 

Characterizing the temporal dynamics and interactions of Adverse Events (AEs) is essential to effectively manage and intervene in clinical practices and patients' treatment. AEs, which are unintended and harmful reactions to treatments, can demonstrate complex and interrelated patterns that traditional analytical methods often fail to capture. These dynamic associations can significantly impact patients' outcomes and may increase the risk of severe complications. Conventional methods fall short in accounting for the temporal and interrelatedness of AEs, which limit their ability to identify critical intervention points in patient treatment.
To address this gap, we propose an exploratory framework, GraphCHASUR, a novel graph-based approach that integrates temporal network analysis, change point detection, and survival analysis to characterize AE dynamics. By modeling AEs as nodes in a graph, with edges representing AEs' co-occurrence weighted by severity and frequency, GraphCHASUR aims to identify latent AE structures and significant shifts in their patterns. Using change-point detection on AE incidence rates to identify key transitions in AE risk may support the development of an early-warning system for clinicians.
Our preliminary analyses suggest that the use of temporal network dynamics and clustering techniques allow us to uncover the patterns of AEs and their dynamic associations which can potentially enable the clinicians and health care providers to intervene with different patients' care strategies. This work aims to bridge the gap in analyzing high-dimensional AE data, paving the way for more robust and interpretable models of patient outcomes.