Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions
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Rudin_Bayesian hierarchical.pdf
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Author(s)
McCormick, Tyler H.
Rudin, Cynthia
Madigan, David
Date Issued
2012
Journal
Annals of Applied Statistics
Publisher
Institute of Mathematical Statistics
Citation
McCormick, Tyler H., Cynthia Rudin, and David Madigan. “Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions.” The Annals of Applied Statistics 6.2 (2012): 652–668. Web.
Version
Author's final manuscript
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.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1214/11-aoas522
