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). Categories and Subject Descriptors J.3 [Life and Medical Sciences]: Health; I.6.5 [Simulation and Modeling]: Model Development General Terms Algorithms Keywords Disease progression modeling, cluster analysis, NIDDK liver transplant database, l...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Ronald K. Pearson, Robert J. Kingan, Alan Hochberg
• Keywords: Data Mining, Disease Progression, Disease Progression Model, KDD 2005, Liver Transplant Database