Bayesian hierarchical rule modeling for predicting medical conditions
Tyler H. McCormick, Cynthia Rudin, and David Madigan
Source: Ann. Appl. Stat. Volume 6, Number 2
(2012), 652-668.
Abstract
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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Permanent link to this document: http://projecteuclid.org/euclid.aoas/1339419611
Digital Object Identifier: doi:10.1214/11-AOAS522
Zentralblatt MATH identifier: 06062734
Mathematical Reviews number (MathSciNet): MR2976486
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