Qualitative reasoning about physical systems has become one of the most productive areas in AI in recent years, due in part to the 1984 special issue of Artificial Intelligence on that topic. My contribution to that issue was a paper entitled "Commonsense reasoning about causality: deriving behavior
Modeling and reasoning with qualitative comparative clinical knowledge
β Scribed by Jianbing Ma; Weiru Liu; Anthony Hunter
- Publisher
- John Wiley and Sons
- Year
- 2010
- Tongue
- English
- Weight
- 173 KB
- Volume
- 26
- Category
- Article
- ISSN
- 0884-8173
No coin nor oath required. For personal study only.
β¦ Synopsis
The number of clinical trials reports is increasing rapidly due to a large number of clinical trials being conducted; it, therefore, raises an urgent need to utilize the clinical knowledge contained in the clinical trials reports. In this paper, we focus on the qualitative knowledge instead of quantitative knowledge. More precisely, we aim to model and reason with the qualitative comparison (QC for short) relations which consider qualitatively how strongly one drug/therapy is preferred to another in a clinical point of view. To this end, first, we formalize the QC relations, introduce the notions of QC language, QC base, and QC profile; second, we propose a set of induction rules for the QC relations and provide grading interpretations for the QC bases and show how to determine whether a QC base is consistent. Furthermore, when a QC base is inconsistent, we analyze how to measure inconsistencies among QC bases, and we propose different approaches to merging multiple QC bases. Finally, a case study on lowering intraocular pressure is conducted to illustrate our approaches.
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