Safe and sound: artificial intelligence in hazardous applications
Safe and sound: artificial intelligence in hazardous applications
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Increasing Human-Organ Transplant Availability: Argumentation-Based Agent Deliberation
IEEE Intelligent Systems
NCI Thesaurus: A semantic model integrating cancer-related clinical and molecular information
Journal of Biomedical Informatics
Argumentation in artificial intelligence
Artificial Intelligence
Harnessing Ontologies for Argument-Based Decision-Making in Breast Cancer
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Elements of Argumentation
ASPARTIX: Implementing Argumentation Frameworks Using Answer-Set Programming
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
Dialectic proof procedures for assumption-based, admissible argumentation
Artificial Intelligence
An argument-based approach to reasoning with clinical knowledge
International Journal of Approximate Reasoning
Argumentation for Aggregating Clinical Evidence
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Argumentation about treatment efficacy
KR4HC'09 Proceedings of the 2009 AIME international conference on Knowledge Representation for Health-Care: data, Processes and Guidelines
Assessing medical treatment compliance based on formal process modeling
USAB'11 Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health
Aggregating evidence about the positive and negative effects of treatments
Artificial Intelligence in Medicine
Argumentation-logic for creating and explaining medical hypotheses
Artificial Intelligence in Medicine
| Hi-index | 0.00 |
Medical practice is increasingly based on the best available evidence, but the volume of information requires many clinicians to rely on systematic reviews rather than the primary evidence. However, these reviews are difficult to maintain, and often do not appear transparent to clinicians reading them. In a previous paper, we have proposed a general language for representing knowledge from clinical trials and a framework that allows reasoning with that knowledge in order to construct and evaluate arguments and counterarguments that aggregate that knowledge. However, clinicians need to feel that such a framework is responsive to their assessment of the strengths and weaknesses of different types of evidence. In this paper, we use a specific version of this existing framework to show how we can capture clinical preferences over types of evidence, and we evaluate this in a pilot study, comparing our system against the choices made by clinicians. This pilot study shows how individual clinicians aggregate evidence based on their preferences over the relative significance of the items of evidence, and it shows how our argumentation system can replicate this behaviour.