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Knowledge-Based Systems
Volume 20, Issue 2, March 2007, Pages 113-119
AI 2006, The 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
 
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doi:10.1016/j.knosys.2006.11.005    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2006 Elsevier B.V. All rights reserved.

Combining task execution and background knowledge for the verification of medical guidelines

Arjen Hommersoma, Corresponding Author Contact Information, E-mail The Corresponding Author, Perry Groota, Peter Lucasa, Michael Balserb and Jonathan Schmittb

aInstitute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands bInstitut für Informatik, Universität Augsburg, Augsburg, Germany

Received 9 October 2006; 
accepted 16 November 2006. 
Available online 8 December 2006.

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Abstract

The use of a medical guideline can be seen as the execution of computational tasks, sequentially or in parallel, in the face of patient data. It has been shown that many of such guidelines can be represented as a ‘network of tasks’, i.e., as a number of steps that have a specific function or goal. To investigate the quality of such guidelines we propose a formalization of criteria for good practice medicine a guideline should comply to. We use this theory in conjunction with medical background knowledge to verify the quality of a guideline dealing with diabetes mellitus type 2 using the interactive theorem prover KIV. Verification using task execution and background knowledge is a novel approach to quality checking of medical guidelines.

Keywords: Medical guidelines; Verification; Logic

Article Outline

1. Introduction
2. Medical guidelines
3. Formalisation of medical guidelines
3.1. Background knowledge
3.2. Medical guidelines in Asbru
3.3. Quality requirements
4. Verification using KIV
4.1. Consistency of background knowledge
4.2. Successful treatment
4.3. Optimality of treatment
4.4. Order of treatments
5. Discussion
References






Knowledge-Based Systems
Volume 20, Issue 2, March 2007, Pages 113-119
AI 2006, The 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
 
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