Real-World Data to Real-World Evidence: Successes, Challenges, and Opportunities
Statistics Seminars: Fall 2025
Department of Mathematical Sciences, IU Indianapolis
Organizer: Honglang Wang (hlwang at iu dot edu)
Talk time: 12:15-1:15pm (EST), 09/02/2025, Tuesday
Zoom Meetings: We host our seminars via zoom meetings: Join from computer or mobile by clicking: Zoom to Join or use Meeting ID: 845 0989 4694 with Password: 113959 to join.
Title: Real-World Data to Real-World Evidence: Successes, Challenges, and Opportunities
Abstract: Real World Data (RWD), including electronic health records (EHR), claims, and billing data, capture detailed, longitudinal patient information, covering demographics, comorbidities, treatments, and outcomes. They have become as a vital data source reflecting real-world patient populations and clinical settings. Applying AI/ML models to RWD have generated insights and real-world evidence (RWE), such as predicting disease onset risk, modeling progression pathways, and supporting more nuanced patient stratification strategies. AI-derived RWE increasingly informs clinical and regulatory decisions.
Bio: Dr. Yu Huang is an assistant professor of the Department of Biostatistics and Health Data Science at Indiana University School of Medicine. His research interests center on the development of artificial intelligence (AI) and machine learning (ML) techniques leveraging real-world data (RWD) to generate novel applications for healthcare systems and improve patient health outcomes. His research spans several critical areas: enhancing machine learning and deep learning strategies in healthcare, specializing in disease progression subphenotyping, predicting health outcomes, implementing ethical AI practices through fair machine learning, and developing health digital twins.
Welcome to join us to learn more about Dr. Huang’s research work via Zoom!