Open Access
June 2012 Bayesian hierarchical rule modeling for predicting medical conditions
Tyler H. McCormick, Cynthia Rudin, David Madigan
Ann. Appl. Stat. 6(2): 652-668 (June 2012). DOI: 10.1214/11-AOAS522


We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient’s possible future medical conditions given the patient’s current and past history of reported conditions. The core of our technique is a Bayesian hierarchical model for selecting predictive association rules (such as “condition 1 and condition 2 → condition 3”) from a large set of candidate rules. Because this method “borrows strength” using the conditions of many similar patients, it is able to provide predictions specialized to any given patient, even when little information about the patient’s history of conditions is available.


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Tyler H. McCormick. Cynthia Rudin. David Madigan. "Bayesian hierarchical rule modeling for predicting medical conditions." Ann. Appl. Stat. 6 (2) 652 - 668, June 2012.


Published: June 2012
First available in Project Euclid: 11 June 2012

zbMATH: 1243.62036
MathSciNet: MR2976486
Digital Object Identifier: 10.1214/11-AOAS522

Keywords: Association rule mining , healthcare surveillance , hierarchical model , machine learning

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 2 • June 2012
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