COMPUTATIONAL HEALTH ECONOMICS
Computational health economics brings statistical advances for big data and data science to answer critical questions in health economics.
- Variable importance of medical conditions in health spending
- Intervening on the data to improve plan payment for disparities
- Medicare risk adjustment with systematically missing data
RECENT RELATED NEWS
A. Shrestha, S. Bergquist, E. Montz, S. Rose (2018). Mental health risk adjustment with clinical categories and machine learning. Health Services Research, in press.
S. Rose (2016). A machine learning framework for plan payment risk adjustment. Health Services Research, 51(6):2358-74. [Link]
A. Mirelman, S. Rose, J. Khan, S. Ahmed, D. Peters, L. Niessen, A. Trujillo (2016). The relationship between noncommunicable disease occurrence and poverty: Evidence from demographic surveillance in Matlab, Bangladesh. Health Policy and Planning, 31(6):785-92. [PDF]