document: Disease progression modeling from historical clinical databases 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 ) .
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