Experienced physicians and automatic generation of decision rules from clinical data

Authors:
William Klement;Szymon Wilk;Martin Michalowski;Ken Farion
Affiliations:
MET Research Group, University of Ottawa, Canada;MET Research Group, University of Ottawa, Canada and Institute of Computing Science, Poznan University of Technology, Poland;Adventium Labs, Minneapolis, MN;Division of Emergency Medicine, Children's Hospital of Eastern Ontario, Canada
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

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Abstract

Clinical Decision Support Systems embed data-driven decision models designed to represent clinical acumen of an experienced physician. We argue that eliminating physicians' diagnostic biases from data improves the overall quality of concepts, which we represent as decision rules. Experiments conducted on prospectively collected clinical data show that analyzing this filtered data produces rules with better coverage, certainty and confirmation. Cross-validation testing shows improvement in classification performance.