Mining Medical Data to Develop Clinical Decision Making Tools in Hemodialysis

Abstract

The main objective of this work is to develop and apply data mining methods for the prediction of patient outcome in nephrology care. Cardiovascular events have an incidence of 20% in the first year of hemodialysis (HD). Real data routinely collected during HD administration were extracted from the Fresenius Medical Care database EuCliD (39 independent variables) and used to develop a random forest predictive model for the forecast of cardiovascular events in the first year of HD treatment. Two feature selection methods were applied. Results of these models in an independent cohort of patients showed a significant predictive ability. Our better result was obtained with a random forest built on 6 variables only (AUC: 77.1% ± 2.9%; MCE: 31.6% ± 3.5%), identified by the variable importance out of bag (OOB) estimate.