Working Group Webinar Library

In this webinar, we will learn what FACMI and FAMIA are, why they matter, and how to prepare for a successful nomination. Presenters

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.

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) project and its results* in the last decade. The focus will be on a study that recruited clinical researchers who used (or did not use) VIADS for data-driven hypothesis generation in the clinical research context. The talk will summarize the experiments and principal findings and share the lessons learned. The role of humans and/or AI in the process and challenges in measuring the processes and results will also be discussed. * Selected publications— PMID: 39819516, 40768504, 40417518, 39211055, 37011112, 38384898, 35736798, 30764811, 24727931, 22195119 Presenter

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.

Explore an AI-driven framework that improves EHR note comprehension and promotes patient empowerment through enhanced health literacy, medical jargon translation, and support for positive behavioral change.