Event-Based Contrastive Learning for Medical Time Series

In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event; for example, the short-term risk of death after an admission for heart failure. This task is challenging due to the complexity, variability, and heterogeneity of longitudinal medical data, especially for individuals suffering from chronic diseases like heart failure. In this paper, we introduce Event-Based Contrastive Learning (EBCL), a method for learning embeddings of heterogeneous patient data that preserves temporal information before and after key index events. We demonstrate that EBCL produces models that yield better fine-tuning performance on critical downstream tasks for a heart failure cohort, including 30-day readmission, 1-year mortality, and 1-week length of stay, relative to other pretraining methods. Our findings also reveal that EBCL pretraining alone can effectively cluster patients with similar mortality and readmission risks, offering valuable insights for clinical decision-making and personalized patient care.