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This research advances machine learning by applying contrastive graph neural networks to model complex temporal dynamics in longitudinal clinical data, enabling robust clustering of disease progression patterns across heterogeneous patient cohorts. The approach leverages structure-aware random walks on temporal graphs to preserve critical context and topology within multivariate trajectories.
Contrastive Representation Learning of Longitudinal Disease Trajectories on Temporal Graphs
arXiv:2607.25609v1 Announce Type: cross Abstract: Understanding disease trajectories from longitudinal clinical data remains challenging due to complex temporal dynamics and heterogeneous patient cohorts. Here, we present a contrastive representation learning framework that models multivariate disease trajectories as temporal graphs and learns representations using contrastive graph neural networks. Nodes represent patient observations over time, while edges capture temporal continuity and structural similarity between trajectories. Structure-aware random walks guide contrastive learning to generate embeddings that preserve temporal context and trajectory topology. The resulting representations enable robust clustering of patients with similar disease progression patterns and reveal latent structure in longitudinal data.
Abstract:Understanding disease trajectories from longitudinal clinical data remains challenging due to complex temporal dynamics and heterogeneous patient cohorts. Here, we present a contrastive representation learning framework that models multivariate disease trajectories as temporal graphs and learns representations using contrastive graph neural networks. Nodes represent patient observations over time, while edges capture temporal continuity and structural similarity between trajectories. Structure-aware random walks guide contrastive learning to generate embeddings that preserve temporal context and trajectory topology. The resulting representations enable robust clustering of patients with similar disease progression patterns and reveal latent structure in longitudinal data.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
arXiv:2607.25609 [cs.LG]
(or arXiv:2607.25609v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.25609 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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