DocumentCode :
2949827
Title :
Argumentation theory in health care
Author :
Longo, Luca ; Kane, Bridget ; Hederman, Lucy
fYear :
2012
fDate :
20-22 June 2012
Firstpage :
1
Lastpage :
6
Abstract :
Argumentation theory (AT) has been gaining momentum in the health care arena thanks to its intuitive and modular way of aggregating clinical evidence and taking rational decisions. The basic principles of argumentation theory are described and demonstrated in the breast cancer recurrence problem. It is shown how to represent available clinical evidence in arguments, how to define defeat relations among them and how to create a formal argumentation framework. Argumentation semantics are then applied over the built-framework to compute arguments justification status. It is demonstrated how this process can enhance the clinician decision-making process. A encouraging predictive capacity is compared against the accuracy rate of well-established machine learning techniques confirming the potential of argumentation theory in health care.
Keywords :
cancer; health care; inference mechanisms; learning (artificial intelligence); AT; argumentation theory; arguments justification status; breast cancer recurrence problem; clinical evidence; clinician decision-making process; formal argumentation framework; health care; machine learning techniques; rational decisions; Breast cancer; Cognition; Decision making; Semantics; Tumors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer-Based Medical Systems (CBMS), 2012 25th International Symposium on
Conference_Location :
Rome
ISSN :
1063-7125
Print_ISBN :
978-1-4673-2049-8
Type :
conf
DOI :
10.1109/CBMS.2012.6266323
Filename :
6266323
Link To Document :
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