Improving adherence to heart failure management guidelines via abductive reasoning*. Issue 5 (23rd August 2017)

• Record Type: Journal Article
• Authors: CHEN, ZHUO; SALAZAR, ELMER; MARPLE, KYLE; GUPTA, GOPAL; TAMIL, LAKSHMAN; CHEERAN, DANIEL; DAS, SANDEEP; AMIN, ALPESH
• Editors: Rocha, Ricardo; Cao Son, Tran
• Is Part Of: Theory and practice of logic programming. Volume 17:Issue 5/6(2017)
• Page Start: 764
• Page End: 779
• Publication Date: 2017-08-23
• Subjects: chronic disease management, -- abduction, -- answer set programming, -- knowledge representation; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068417000308
• Languages: English
• ISSNs: 1471-0684

Abstract

Abstract: Management of chronic diseases, such as heart failure, is a major public health problem. A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that all physicians should follow. Due to their complexity, these guidelines are difficult to implement and are adopted slowly by the medical community at large. We have developed a physician advisory system that codes the entire set of clinical practice guidelines for managing heart failure using answer set programming. In this paper, we show how abductive reasoning can be deployed to find missing symptoms and conditions that the patient must exhibit in order for a treatment prescribed by a physician to work effectively. Thus, if a physician does not make an appropriate recommendation or makes a non-adherent recommendation, our system will advise the physician about symptoms and conditions that must be in effect for that recommendation to apply. It is under consideration for acceptance in TPLP.