The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 6, Number 2 (2012), 652-668.
Bayesian hierarchical rule modeling for predicting medical conditions
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.
Ann. Appl. Stat., Volume 6, Number 2 (2012), 652-668.
First available in Project Euclid: 11 June 2012
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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
- Supplementary material: Additional simulation results. In the supplement we present additional simulation results which speak to the performance of HARM.