Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions

McCormick, Tyler H.; Rudin, Cynthia; Madigan, David B.

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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Also Published In

Annals of Applied Statistics

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Academic Units
Institute of Mathematical Statistics
Published Here
May 14, 2014