Automating Disease Management Using Answer Set Programming

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dc.contributor.authorChen, Zhuoen_US
dc.contributor.utdAuthorChen, Zhuoen_US
dc.date.accessioned2018-09-24T15:03:22Z
dc.date.available2018-09-24T15:03:22Z
dc.date.created2016en_US
dc.date.issued2018-09-24
dc.description.abstractManagement 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. © Zhuo Chen.en_US
dc.description.departmentSchool of Natural Sciences and Mathematicsen_US
dc.identifier.bibliographicCitationChen, Z.. 2016. "Automating disease management using answer set programming." Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016) no. 22, doi:10.4230/OASIcs.ICLP.2016.22en_US
dc.identifier.issn2190-6807en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/6086
dc.identifier.volume22en_US
dc.publisherDagstuhl Publishingen_US
dc.relation.urihttp://dx.doi.org/10.4230/OASIcs.ICLP.2016.22en_US
dc.rightsCC BY 4.0 (Attribution)en_US
dc.rights©2016 Zhuo Chenen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceTechnical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)
dc.subjectChronic diseases--Treatmenten_US
dc.subjectReasoningen_US
dc.subjectReasoning--Data processingen_US
dc.subjectMedical protocolsen_US
dc.subjectPhysician practice patternsen_US
dc.titleAutomating Disease Management Using Answer Set Programmingen_US
dc.type.genrearticleen_US

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