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Deploying NLP and machine learning in real-world health systems requires far more than model development—it demands scalable infrastructure, rigorous evaluation, and integration within complex operational environments.

The VaxHesSTL framework combines Graph and Recurrent Neural Networks to predict vaccine hesitancy at the ZIP code level by capturing both spatial relationships and historical trends, outperforming existing models when trained on a six-year, five-million-person insurance claims dataset from Virginia. To address the high cost of such data, the research also explores an active learning approach to optimize which ZIP codes are selected for training.

Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. However, the process of hypothesis generation is less understood. In this talk, Dr. Jing will briefly introduce the VIADS (a Visual Interactive Analytic tool for filtering and summarizing large health Data Sets coded with hierarchical terminologies) [...]