Abstract

http://ssrn.com/abstract=1736062
 
 

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A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction


Tyler McCormick


Columbia University - Department of Statistics

Cynthia Rudin


Massachusetts Institute of Technology (MIT) - Management Science (MS)

David Madigan


Columbia University - Department of Statistics

January 6, 2011

MIT Sloan Research Paper

Abstract:     
In many healthcare settings, patients visit healthcare professionals periodically and report multiple medical conditions, or symptoms, at each encounter. We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient's possible future symptoms given the patient's current and past history of reported symptoms. The core of our technique is a Bayesian hierarchical model for selecting predictive association rules (such as "symptom 1 and symptom 2 → symptom 3") from a large set of candidate rules. Because this method "borrows strength" using the symptoms 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 symptoms is available.

Number of Pages in PDF File: 19

Keywords: Hierarchical Bayesian Modeling, Association Rules, Medical Symptom Prediction

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Date posted: January 8, 2011 ; Last revised: January 30, 2011

Suggested Citation

McCormick, Tyler and Rudin, Cynthia and Madigan, David, A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction (January 6, 2011). MIT Sloan Research Paper . Available at SSRN: http://ssrn.com/abstract=1736062 or http://dx.doi.org/10.2139/ssrn.1736062

Contact Information

Tyler McCormick (Contact Author)
Columbia University - Department of Statistics ( email )
Mail Code 4403
New York, NY 10027
United States
Cynthia Rudin
Massachusetts Institute of Technology (MIT) - Management Science (MS) ( email )
United States
David Madigan
Columbia University - Department of Statistics ( email )
Mail Code 4403
New York, NY 10027
United States
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