Data-Driven Disease Progression Modeling
Kenney Ng, Mohamed F. Ghalwash, Prithwish Chakraborty, Daby Sow, Akira Koseki, Hiroki Yanagisawa, Michiharu Kudo · Health informatics · 2022
This chapter provides a comprehensive overview to data driven disease progression modeling techniques. It adopts a broad approach to disease progression, focusing on all computational methods able to model any temporal aspects of disease progression. Consequently, we have focused on three classes of analysis: staging and trajectory estimation analysis to better understand the course of a disease, predictive classification analysis for important disease related event prediction, and time to event analysis with survival models to estimate when clinically significant events are expected to occur during the progression of a disease. We describe the state of the art in each of these classes, together with discussions on challenges and opportunities for additional research.