Boosting Patient Representation Learning via Graph Contrastive Learning

Abstract

Building deep neural network models for clinical prediction tasks is an increasingly active area of research. While existing approaches show promising performance, the learned patient representations from deep neural networks are often task-specific and not generalizable across multiple clinical prediction tasks. In this paper, we propose a novel neural network architecture leveraging the graph contrastive learning paradigm to learn patient representations that are applicable to a wide range of clinical prediction tasks. In particular, our approach consists of three well-designed modules for learning graph-based patient representations, alongside a pretraining mechanism that exploits self-supervised information in generated patient graphs. These modules collaboratively integrate patient graph structure learning, refinement, and contrastive learning, enhanced by masked graph modeling as a pretraining mechanism to optimize learning outcomes. Empirical results show that the proposed approach outperforms baselines in both self-supervised and supervised learning scenarios, offering robust, effective, and more generalizable patient representations in healthcare applications.