Structured Data for Health

Structured data is the backbone of modern healthcare, encompassing tabular Electronic Health Records (EHRs), high-frequency time-series biosignals, and complex disease networks. Despite the critical need for holistic patient modeling, research across these modalities remains largely siloed, often overlooking the multimodal nature of real-world clinical decision-making. The "Structured Data for Health" workshop aims to bridge this gap by establishing a unified forum for the convergence of tabular, time-series, and graph-based health data research. We focus on addressing shared technical challenges—such as data heterogeneity, sparsity, and distribution shifts—while leveraging emerging capabilities in Large Language Models (LLMs) for data structuring and reasoning. Featuring a globally diverse lineup of speakers from leading academic and industry institutions, this workshop will cover the full spectrum of structured health AI, from foundational representation learning and multimodal fusion to trustworthy, real-world clinical deployment.