Application of Information-Theoretic Data Mining Techniques in a National Ambulatory Practice Outcomes Research Network

Authors

Adam Wright, Oregon Health & Science University
Thomas N. Ricciardi, Oregon Health & Science University
Martin Zwick, Portland State UniversityFollow

Document Type

Conference Proceeding

Publication Date

10-2005

Subjects

Reconstructability Analysis, Information Theory, Probabilistic graphical modeling, Multivariate analysis discrete multivariate modeling, Data mining

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.

Description

This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose.

Presented at the American Medical Informatics Association annual symposium, October 22-26, 2005, Washington, DC

Persistent Identifier

https://archives.pdx.edu/ds/psu/27213

Citation Details

Wright, A., Ricciardi, T. N., & Zwick, M. (2005). Application of information-theoretic data mining techniques in a national ambulatory practice outcomes research network. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2005, 829-33.