• 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