2018 international conference san antonio
Session 3 – Hong Kan – A Comparison of Risk Adjustment Models Using Traditional Statistical and Machine Learning Techniques in Predicting Health Care Costs in Older Adults
A Comparison of Risk Adjustment Models Using Traditional Statistical and Machine Learning Techniques in Predicting Health Care Costs in Older Adults
Hong Kan, PhD, MPP, MA
Center for Population Health IT
Bloomberg School of Public Health
Johns Hopkins University
Read bio
Hong Kan, PhD, MA, MPP is an associate scientist in the Department of Health Policy and Management at Johns Hopkins Bloomberg School of Public Health and a core faculty member at CPHIT. He has a background in health services research, statistics, and machine learning. Before joining Johns Hopkins, he had over a decade of outcomes research experience in the managed care and pharmaceutical industries. His primary research interests lie in outcomes research, health economics, risk adjustment, and related methodologies including predictive analytics and causal inference. He brings academic research and methodological expertise along with broad industry research experience and real-world perspectives.
Please log in/register to access.
Menu
