
- This event has passed.
Zilber College of Public Health Weekly Seminar – Interpretable Fall Risk Prediction Using Explainable AI and Large Language Models, Jake Luo, Associate Professor – Health Care Informatics
February 13 @ 9:00 am - 9:50 am
Free
Interpretable Fall Risk Prediction Using Explainable AI and Large Language Models
Presented by Jake Luo, PhD
Associate Professor, Health Care Informatics
Zilber College of Public Health
This presentation introduces an innovative approach to fall risk prediction, combining state-of-the-art machine learning with explainable AI and natural language generation. The system utilizes electronic health records to predict fall risk, employs SHAP values for interpretability, and leverages large language models to generate clear, actionable explanations. The research demonstrates improved accuracy and transparency in fall risk assessment, empowering healthcare providers with actionable insights for more effective prevention strategies.
Join us in a forum to hear the latest in the research and practice of public health to support a just, equitable, healthy future for people, communities, and the environment in Milwaukee, the state of Wisconsin, and beyond.
The seminar can also be attended virtually on Teams.