A Physician Advisory System for Chronic Heart Failure management based on knowledge patterns. Issue 5 (14th October 2016)

• Record Type: Journal Article
• Authors: CHEN, ZHUO; MARPLE, KYLE; SALAZAR, ELMER; GUPTA, GOPAL; TAMIL, LAKSHMAN
• Editors: Carro, Manuel; King, Andy
• Is Part Of: Theory and practice of logic programming. Volume 16:Issue 5/6(2016)
• Page Start: 604
• Page End: 618
• Publication Date: 2016-10-14
• Subjects: chronic disease management, -- knowledge pattern, -- answer set programming, -- knowledge representation, -- automated reasoning; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068416000429
• Languages: English
• ISSNs: 1471-0684

Abstract

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.