License: Creative Commons Attribution 3.0 Unported license (CC-BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ICLP.2016.22
URN: urn:nbn:de:0030-drops-67502
URL: https://drops.dagstuhl.de/opus/volltexte/2016/6750/
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Chen, Zhuo

Automating Disease Management Using Answer Set Programming

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OASIcs-ICLP-2016-22.pdf (0.3 MB)


Abstract

Management of chronic diseases such as heart failure, diabetes, and chronic obstructive pulmonary disease (COPD) is a major problem in health care. A standard approach that the medical community has devised to manage widely prevalent chronic diseases such as chronic heart failure (CHF) 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 either ignored or not complied with at all, which can result in poor medical practices. It is not even clear whether it is humanly possible to follow these guidelines due to their length and complexity. In the case of CHF management, the guidelines run nearly 80 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. Our approach is based on developing reasoning templates (that we call knowledge patterns) and using these patterns 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 will work even in the presence of incomplete information. Our work makes two contributions: (i) it shows that highly complex guidelines can be successfully coded as ASP rules, and (ii) it develops a series of knowledge patterns that facilitate the coding of knowledge expressed in a natural language and that can be used for other application domains.

BibTeX - Entry

@InProceedings{chen:OASIcs:2016:6750,
  author =	{Zhuo Chen},
  title =	{{Automating Disease Management Using Answer Set Programming}},
  booktitle =	{Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)},
  pages =	{22:1--22:10},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-007-1},
  ISSN =	{2190-6807},
  year =	{2016},
  volume =	{52},
  editor =	{Manuel Carro and Andy King and Neda Saeedloei and Marina De Vos},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2016/6750},
  URN =		{urn:nbn:de:0030-drops-67502},
  doi =		{10.4230/OASIcs.ICLP.2016.22},
  annote =	{Keywords: chronic disease management, knowledge pattern, answer set programming, knowledge representation, automated reasoning}
}

Keywords: chronic disease management, knowledge pattern, answer set programming, knowledge representation, automated reasoning
Collection: Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)
Issue Date: 2016
Date of publication: 11.11.2016


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