An argument-based approach to reasoning with clinical knowledge

Gorogiannis, Nikos ORCID: https://orcid.org/0000-0001-8660-6609, Hunter, Anthony and Williams, Matthew (2009) An argument-based approach to reasoning with clinical knowledge. International Journal of Approximate Reasoning, 51 (1) . pp. 1-22. ISSN 0888-613X [Article] (doi:10.1016/j.ijar.2009.06.015)

[img]
Preview
PDF - Final accepted version (with author's formatting)
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives.

Download (253kB) | Preview

Abstract

Better use of biomedical knowledge is an increasingly pressing concern for tackling challenging diseases and for generally improving the quality of healthcare. The quantity of biomedical knowledge is enormous and it is rapidly increasing. Furthermore, in many areas it is incomplete and inconsistent. The development of techniques for representing and reasoning with biomedical knowledge is therefore a timely and potentially valuable goal. In this paper, we focus on an important and common type of biomedical knowledge that has been obtained from clinical trials and studies. We aim for (1) a simple language for representing the results of clinical trials and studies; (2) transparent reasoning with that knowledge that is intuitive and understandable to users; and (3) simple computation mechanisms with this knowledge in order to facilitate the development of viable implementations. Our approach is to propose a logical language that is tailored to the needs of representing and reasoning with the results of clinical trials and studies. Using this logical language, we generate arguments and counterarguments for the relative merits of treatments. In this way, the incompleteness and inconsistency in the knowledge is analysed via argumentation. In addition to motivating and formalising the logical and argumentation aspects of the framework, we provide algorithms and computational complexity results.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 15919
Useful Links:
Depositing User: Nikos Gkorogiannis
Date Deposited: 12 May 2015 13:40
Last Modified: 16 Nov 2019 20:47
URI: https://eprints.mdx.ac.uk/id/eprint/15919

Actions (login required)

View Item View Item

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year