Argumentation for Aggregating Clinical Evidence.
In: Gregoire, E, (ed.)
22ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2010), PROCEEDINGS, VOL 1.
(pp. ? - ?).
IEEE COMPUTER SOC
Evidence-based decision making is becoming increasingly important in healthcare. Much valuable evidence is in the form of the results from clinical trials that compare the relative merits of treatments. For this, in previous papers , , we have proposed a general framework for representing and synthesizing knowledge from clinical trials involving the same outcome indicator. Now, in this paper, we present a new framework for representing and synthesizing knowledge from clinical trials involving multiple outcome indicators. In this framework, evidence from randomized clinical trials, systematic reviews, meta-analyses, network analyses, etc., comparing a pair of treatments tau(1) and tau(2) according to desired and/or undesired outcomes is aggregated to give an overall evaluation of the treatments saying tau(1) is superior to tau(2), or tau(1) is equivalent to tau(2), or tau(1) is inferior to tau(2). Our general framework incorporates inference rules for generating arguments and counterarguments for claiming that one treatment is superior to another based on the available evidence, and preference rules for specifying which arguments are preferred. In this paper, we also present a new version of this framework that incorporates utility-theoretic criteria for defining specific preference rules over arguments.
|Title:||Argumentation for Aggregating Clinical Evidence|
|Event:||22nd International Conference on Tools with Artificial Intelligence|
|Dates:||2010-10-27 - 2010-10-29|
|Keywords:||Logical argumentation, Knowledge aggregation, Decision-support systems, Evidence-based medicine, SUPPORT|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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