Modeling disease progression via fused sparse group lasso
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper considers the problem of modeling disease progression from historical clinical databases, with the ultimate objective of stratifying patients into groups with clearly distinguishable prognoses or suitability for different treatment strategies. To meet this objective, we describe a procedure that first fits clinical variables measured over time to a disease progression model. The resulting parameter estimates are then used as the basis for a stepwise clustering procedure to stratify patients into groups with distinct survival characteristics. As a practical illustration, we apply this procedure to survival prediction, using a liver transplant database from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).