Application of information-theoretic data mining techniques in a national ambulatory practice outcomes research network.
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.
Wright
Adam
A
Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, USA.
Ricciardi
Thomas N
TN
Zwick
Martin
M
D000328:Adult; D000553:Ambulatory Care; D001185:Artificial Intelligence; D016208:Databases, Factual; D048909:Diabetes Complications; D003922:Diabetes Mellitus, Type 1; D003924:Diabetes Mellitus, Type 2; D005260:Female; D006801:Humans; D016247:Information Storage and Retrieval; D008297:Male; D008498:Medical Record Linkage; D016347:Medical Records Systems, Computerized; D009203:Myocardial Infarction / Q000209:etiology; D017063:Outcome Assessment, Health Care; D011787:Quality of Health Care; D012307:Risk Factors; D012720:Severity of Illness Index