• DocumentCode
    2913944
  • Title

    Mining answers for causal questions in a medical example

  • Author

    Sobrino, A. ; Olivas, J.A. ; Puente, C.

  • Author_Institution
    Fac. of Philos., Univ. of Santiago de Compostela, Santiago de Compostela, Spain
  • fYear
    2011
  • fDate
    22-24 Nov. 2011
  • Firstpage
    432
  • Lastpage
    437
  • Abstract
    The aim of this paper is to approach causal questions in a medical domain. Causal questions par excellence are what, how and why-questions. The `pyramid of questions´ shows this. At the top, why-questions are the prototype of causal questions. Usually why-questions are related to scientific explanations. Although cover law explanation is characteristically of physical sciences, it is less common in biological or medical knowledge. In medicine, laws applied to all cases are rare. It seems that doctors express their knowledge using mechanisms instead of natural laws. In this paper we will approach causal questions with the aim of: (1) answering what-questions as identifying the cause of an effect; (2) answering how-questions as selecting an appropriate part of a mechanism that relates pairs of cause-effect (3) answering why-questions as identifying ultimate causes in the answers of how-questions. In this task, we hypothesize that why-questions are related to scientific explanations in a negative and a positive note: (i) as previously said, scientific explanations in biology are based on mechanisms instead of natural laws; (ii) scientific explanations are generally concerned with deepening, providing explanations as detailed as possible. Thus, we conjecture that answers to why-questions have to find the ultimate causes in a mechanism and link them to the prior cause summarizing the intermediate nodes in order to provide a comprehensible answer. The Mackie´s INUS causality offers a theoretical support for this solution.
  • Keywords
    causality; cause-effect analysis; data mining; medical computing; medicine; question answering (information retrieval); Mackie´s INUS causality; biological knowledge; causal questions par excellence; cause-effect answering why-questions; cover law explanation; how-questions answering; intermediate nodes; medical domain; medical example; medical knowledge; medicine; mining answers; natural laws; what-questions; Blood; Cancer; Databases; Intelligent systems; Joining processes; Lungs; Materials; answering causal questions; causal questions; imperfect causality; mechanisms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on
  • Conference_Location
    Cordoba
  • ISSN
    2164-7143
  • Print_ISBN
    978-1-4577-1676-8
  • Type

    conf

  • DOI
    10.1109/ISDA.2011.6121694
  • Filename
    6121694