Predicting survival time for kidney dialysis patients: a data mining approach
Andrew Kusiaka b✉, Bradley S. Dixonc, Shital C. Shahb
- aIntelligent Systems Laboratory, College of Engineering, 3131 Seamans Center, Iowa City, Iowa 52242 1527, USA. andrew-kusiak@uiowa.edu
- bIntelligent Systems Laboratory, College of Engineering, 3131 Seamans Center, Iowa City, Iowa 52242 1527, USA
- cUniversity of Iowa Hospital and Clinics, E300D GH, The University of Iowa, Iowa City, Iowa 52242 1081, USA
- ✉Corresponding author
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Abstract
The cost for providing care for patients on hemodialysis due to end stage kidney disease is high. Finding ways to improve patient outcomes and reduce the cost of dialysis is important. Dialysis care is intricate and multiple factors may influence patient survival. Over 50 parameters may be monitored on a regular basis in providing kidney dialysis treatments. Understanding the collective role of these parameters in determining outcomes for an individual patient and administering individualized treatments allowing specific interventions is a challenge. Individual patient survival may depend on a complex interrelationship between multiple demographic and clinical parameters, medications, medical interventions, and the dialysis treatment prescription. In this research, data preprocessing, data transformations, and a data mining approach are used to elicit knowledge about the interaction between many of these measured parameters and patient survival. Two different data mining algorithms were employed for extracting knowledge in the form of decision rules. These rules were used by a decision-making algorithm, which predicts survival of new unseen patients. Important parameters identified by data mining are interpreted for their medical significance. The concepts introduced in this research have been applied and tested using data collected at four dialysis sites. The computational results are reported in the paper.
TL;DRTwo different data mining algorithms were employed for extracting knowledge in the form of decision rules that were used by a decision-making algorithm, which predicts survival of new unseen patients. (Semantic Scholar)