Skip to main content
Kent Academic Repository

Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis

Williams, Matthew, Williamson, Jon (2006) Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis. Journal of Logic, Language and Information, 15 (1/2). pp. 155-178. ISSN 0925-8531. (doi:10.1007/s10849-005-9010-x) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:7449)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1007/s10849-005-9010-x

Abstract

We present a new framework for combining logic with probability, and demonstrate the application of this framework to breast cancer prognosis. Background knowledge concerning breast cancer prognosis is represented using logical arguments. This background knowledge and a database are used to build a Bayesian net that captures the probabilistic relationships amongst the variables. Causal hypotheses gleaned from the Bayesian net in turn generate new arguments. The Bayesian net can be queried to help decide when one argument attacks another. The Bayesian net is used to perform the prognosis, while the argumentation framework is used to provide a qualitative explanation of the prognosis.

Item Type: Article
DOI/Identification number: 10.1007/s10849-005-9010-x
Subjects: R Medicine > R Medicine (General)
B Philosophy. Psychology. Religion > B Philosophy (General)
B Philosophy. Psychology. Religion > BC Logic
Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Divisions: Divisions > Division of Arts and Humanities > School of Culture and Languages
Depositing User: Jon Williamson
Date Deposited: 30 Oct 2008 18:35 UTC
Last Modified: 16 Nov 2021 09:45 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/7449 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.