The Annals of Applied Statistics

Bayesian hierarchical rule modeling for predicting medical conditions

Tyler H. McCormick, Cynthia Rudin, and David Madigan

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat., Volume 6, Number 2 (2012), 652-668.

Dates
First available in Project Euclid: 11 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1339419611

Digital Object Identifier
doi:10.1214/11-AOAS522

Mathematical Reviews number (MathSciNet)
MR2976486

Zentralblatt MATH identifier
1243.62036

Keywords
Association rule mining healthcare surveillance hierarchical model machine learning

Citation

McCormick, Tyler H.; Rudin, Cynthia; Madigan, David. Bayesian hierarchical rule modeling for predicting medical conditions. Ann. Appl. Stat. 6 (2012), no. 2, 652--668. doi:10.1214/11-AOAS522. https://projecteuclid.org/euclid.aoas/1339419611


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