Improving risk grouping rules for prostate cancer patients with optimization
Published 1 January 2004
Leonid Churilov, Adil Bagirov, Daniel Schwartz, Kate Smith‐Miles, Michael Dally
Citations13
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TL;DR
It is demonstrated that using the optimization based approach to data mining of a prostate cancer patients database can lead to generation of a significant amount of new knowledge that can be effectively utilized to enhance clinical decision making.
Abstract
Data mining techniques provide a popular and powerful toolset to address both clinical and management issues in the area of health care. This paper describes the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. The cluster analysis based model is suggested and an application of non-smooth non-convex optimization techniques to solve this model is discussed. It is demonstrated that using the optimization based approach to data mining of a prostate cancer patients database can lead to generation of a significant amount of new knowledge that can be effectively utilized to enhance clinical decision making.
Keywords
Computer ScienceMathematicsDecision Sciences
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