Search AMIA.org

Large language models are transforming clinical NLP, but real-world healthcare pipelines still combine LLMs with traditional methods to turn clinical notes and patient narratives into structured evidence. This talk covers two active projects using this approach — one studying medication adherence across five diseases, another improving drug safety prediction — and discusses best practices for evaluating LLM systems in clinical settings.

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

Nursing education is already using AI, but are we teaching it intentionally, ethically, and effectively? Join informatics leaders and interdisciplinary experts as we explore AI competencies, real teaching examples, and what AACN should consider next. Watch the Webinar 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.

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) [...]