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A Physician Advisory System for Chronic Heart Failure management based on knowledge patterns

Published online by Cambridge University Press:  14 October 2016

ZHUO CHEN ,
KYLE MARPLE ,
ELMER SALAZAR ,
GOPAL GUPTA  and
LAKSHMAN TAMIL
ZHUO CHEN
Affiliation:
University of Texas at Dallas, Texas, U.S.A. (e-mail: [email protected], [email protected], [email protected], [email protected], [email protected])
KYLE MARPLE
Affiliation:
University of Texas at Dallas, Texas, U.S.A. (e-mail: [email protected], [email protected], [email protected], [email protected], [email protected])
ELMER SALAZAR
Affiliation:
University of Texas at Dallas, Texas, U.S.A. (e-mail: [email protected], [email protected], [email protected], [email protected], [email protected])
GOPAL GUPTA
Affiliation:
University of Texas at Dallas, Texas, U.S.A. (e-mail: [email protected], [email protected], [email protected], [email protected], [email protected])
LAKSHMAN TAMIL
Affiliation:
University of Texas at Dallas, Texas, U.S.A. (e-mail: [email protected], [email protected], [email protected], [email protected], [email protected])
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Abstract

Management of chronic diseases such as chronic heart failure (CHF) is a major problem in health care. A standard approach followed by the medical community is to have a committee of experts develop guidelines that all physicians should follow. These guidelines typically consist of a series of complex rules that make recommendations based on a patient's information. Due to their complexity, often the guidelines are ignored or not complied with at all. It is not even clear whether it is humanly possible to follow these guidelines due to their length and complexity. For instance, for CHF, the guidelines run nearly eighty pages. In this paper we describe a physician-advisory system for CHF management that codes the entire set of clinical practice guidelines for CHF using answer set programming (ASP). Our approach is based on developing reasoning templates, that we call knowledge patterns, and using them to systemically code the clinical guidelines for CHF as ASP rules. Use of the knowledge patterns greatly facilitates the development of our system. Given a patient's medical information, our system generates a recommendation for treatment just as a human physician would, using the guidelines. Our system works even in the presence of incomplete information.

Keywords

chronic disease managementknowledge patternanswer set programmingknowledge representationautomated reasoning
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 16 , Special Issue 5-6: 32nd International Conference on Logic Programming , September 2016 , pp. 604 - 618
Copyright
Copyright © Cambridge University Press 2016 

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