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

By Zhuo Chen, Kyle Marple, Elmer Salazar, Gopal Gupta, Lakshman S. Tamil. 2016

76 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

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In Artificial Intelligence

  1. Jimeng Sun
  2. Walter F. Stewart
  3. Robert Chen
  4. Yutao Zhang
  5. Jie Tang
  6. Cao Xiao
  7. Charu C. Aggarwal
  8. Edward Choi

In Molecular Biology

  1. Jimeng Sun
  2. Jie Tang
  3. Walter F. Stewart

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