Aggregating evidence about the positive and negative effects of treatments using a computational model of argument.

Aggregating evidence about the positive and negative effects of treatments using a computational model of argument.

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Dr Matthew Williams

Honorary Clinical Senior Lecturer & Consultant Clinical Oncologist

Faculty of Medicine, Department of Surgery & Cancer

Imperial College London


Prof Anthony Hunter

Professor of Artificial Intelligence

Department of Computer Science,

University College London



ABSTRACT: Computational models of argument are being developed to capture aspects of how we can handle incomplete and inconsistent information through the use of argumentation. In this talk, we describe a novel approach to aggregating clinical evidence using a computational model of argument [1,2]. The framework is a formal approach to synthesizing knowledge from clinical trials involving multiple outcome indicators. Based on the available evidence, arguments are generated for claiming that one treatment is superior, or equivalent, to another. Evidence comes from randomized clinical trials, systematic reviews, meta-analyses, network analyses, etc. Preference criteria over arguments are used that are based on the outcome indicators, and the magnitude of those outcome indicators, in the evidence. Meta-arguments attack (i.e they are counterarguments to) arguments that are based on weaker evidence. An evaluation criterion is used to determine which are the winning arguments, and thereby the recommendations for which treatments are superior. We have compared our approach with recommendations made in NICE Guidelines, and we have used our approach to publish a more refined systematic review of evidence presented in a Cochrane Review [3]. Our approach has an advantage over meta-analyses and network analyses in that they aggregate evidence according to a single outcome indicator, whereas our approach combines evidence according to multiple outcome indicators.

[1] A Hunter and M Williams (2012) Aggregating evidence about the positive and negative effects of treatments, Artificial Intelligence in Medicine, 56:173-190

[2] A Hunter and M Williams (2015) Aggregation of Clinical Evidence using Argumentation: A Tutorial Introduction, Foundations of Biomedical Knowledge Representation, edited by Arjen Hommersom and Peter Lucas, LNCS volume 9521 , pages 317-337, Springer

[3] M Williams, Z. Liu, A.Hunter and F. MacBeth (2015) An updated systematic review of lung chemo-radiotherapy using a new evidence aggregation method, Lung Cancer 87(3):290-5


This Seminar is supported by the Complex Reviews Support Unit, funded by the National Institute for Health Research (project number 14/178/29)