Abstract
Health time series (HTS) provide a fundamental representation of dynamic health processes, spanning organ-level physiological signals, patient-level clinical trajectories, and population-level epidemic dynamics. Despite their shared temporal nature, existing surveys often study these categories separately, leaving their methodological connections underdeveloped. In this survey, we provide a unified review of deep learning for HTS, organized around a scarcity-driven methodological development trajectory. Specifically, we examine how label scarcity motivates task-specific supervised learning with domain-aware temporal modeling; how sample scarcity motivates foundation model development and generative augmentation; and how information scarcity motivates multi-modal learning by incorporating complementary clinical information. Finally, we summarize representative healthcare applications and open research directions. By connecting data categories, scarcity challenges, and methodological paradigms, this survey provides a structured roadmap for understanding and developing deep learning methods for HTS.