Argumentation for Aggregating Clinical Evidence

Abstract--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 [1], [2], 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 1 and 2 according to desired and/or undesired outcomes is aggregated to give an overall evaluation of the treatments saying 1 is superior to 2, or 1 is equivalent to 2, or 1 is inferior to 2. Our general framework incorporates inference rules for generating arguments and counterarguments for claiming that...

• Type: Journal
• Year: 2010
• Where: ICTAI
• Authors: Anthony Hunter, Matthew Williams
• Keywords: Artificial Intelligence, Clinical Trials, General Framework, ICTAI 2010, Outcome Indicators