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Journal of Healthcare Engineering
Volume 2 (2011), Issue 1, Pages 55-74
Research Article

Interpretable Predictive Models for Knowledge Discovery from Home-Care Electronic Health Records

Bonnie L. Westra,1 Sanjoy Dey,2 Gang Fang,2 Michael Steinbach,2 Vipin Kumar,2 Cristina Oancea,3 Kay Savik,1 and Mary Dierich1

1School of Nursing, University of Minnesota, Minneapolis, MN 55455, USA
2Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
3School of Public Health, Division of Environmental Health Sciences, University of Minnesota, Minneapolis, MN 55455, USA

Received 1 June 2010; Accepted 1 December 2010

Copyright © 2011 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The purpose of this methodological study was to compare methods of developing predictive rules that are parsimonious and clinically interpretable from electronic health record (EHR) home visit data, contrasting logistic regression with three data mining classification models. We address three problems commonly encountered in EHRs: the value of including clinically important variables with little variance, handling imbalanced datasets, and ease of interpretation of the resulting predictive models. Logistic regression and three classification models using Ripper, decision trees, and Support Vector Machines were applied to a case study for one outcome of improvement in oral medication management. Predictive rules for logistic regression, Ripper, and decision trees are reported and results compared using F-measures for data mining models and area under the receiver-operating characteristic curve for all models. The rules generated by the three classification models provide potentially novel insights into mining EHRs beyond those provided by standard logistic regression, and suggest steps for further study.