Application of Information-Theoretic Data Mining Techniques in a National

Ambulatory Practice Outcomes Research Network


Adam Wright(a), Thomas N. Ricciardi(a,b) and Martin Zwick(c)

(a) Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland Oregon USA

(b) GE Healthcare Technologies, Waukesha Wisconsin USA

(c) Systems Science Ph.D. Program, Portland State University, Portland Oregon USA

 

Keywords: Data Warehouse, Data Mining, Reconstructability Analysis, Ambulatory Electronic Medical Records.


Abstract

The Medical Quality Improvement Consortium data warehouse contains de-identified data on more than 3.6 million patients including their problem lists, test results, procedures and medication lists. This study uses reconstructability analysis, an information-theoretic data mining technique, on the MQIC data warehouse to empirically identify risk factors for various complications of diabetes including myocardial infarction and microalbuminuria. The risk factors identified match those risk factors identified in the literature, demonstrating the utility of the MQIC data warehouse for outcomes research, and RA as a technique for mining clinical data warehouses.


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