Improving adherence to heart failure management guidelines via abductive reasoning

Zhuo Chen, Elmer Salazar, Kyle Marple, Gopal Gupta, Lakshman Tamil, Daniel Cheeran, Sandeep Das, Alpesh Amin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)764-779
Number of pages16
JournalTheory and Practice of Logic Programming
Issue number5-6
StatePublished - Sep 1 2017


  • abduction
  • answer set programming
  • chronic disease management
  • knowledge representation

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics
  • Artificial Intelligence


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