Sequential Medical Treatment Mining for Survival Analysis

Arlei SilvaWagner Meira JrOdilon QueirozMariângela Cherchiglia

In this paper, we study the problem of evaluating the survival associated with sequential medical treatments. We propose a new data mining algorithm (SMTM) that combines the survival analysis framework with the sequence mining task. This research is motivated by the necessity of assessing the quality of the renal replacement therapies (RRTs), what has become a policy issue in several countries. We apply SMTM to evaluate sequences of RRTs and show that SMTM is computationally efficient and able to provide important knowledge about the survival of patients in RRT, better describing the patients' survival pattern than the traditional survival analysis. The results obtained may support future programs and health policies for the assistance of patients in RRT.

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