Interpretable predictive models for knowledge discovery from home-care electronic health records

Bonnie L Westra, Sanjoy Dey, Gang Fang, Michael S Steinbach, Vipin Kumar, Cristina Oancea, Kay Savik, Mary T Dierich

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)55-74
Number of pages20
JournalJournal of healthcare engineering
Issue number1
StatePublished - Mar 2011


  • Data mining
  • Electronic health records
  • Home care
  • Oral medication management
  • Rules classification


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