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Analysis of medical arguments from patient experiences expressed on the social web

Noor, K; Hunter, A; Mayer, A; (2017) Analysis of medical arguments from patient experiences expressed on the social web. In: IEA/AIE 2017: Advances in Artificial Intelligence: From Theory to Practice. (pp. pp. 285-294). Springer, Cham Green open access

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Abstract

In this paper we present an implemented method for analysing arguments from drug reviews given by patients in medical forums on the web. For this we provide a number of classification rules which allow for the extraction of specific arguments from the drug reviews. For each review we use the extracted arguments to instantiate a Dung argument graph. We undertake an evaluation of the resulting argument graphs by applying Dung’s grounded semantics. We demonstrate a correlation between the arguments in the grounded extension of the graph and the rating provided by the user for that particular drug.

Type: Proceedings paper
Title: Analysis of medical arguments from patient experiences expressed on the social web
Event: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
ISBN-13: 9783319600444
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-60045-1_31
Publisher version: http://dx.doi.org/10.1007/978-3-319-60045-1 31
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1573473
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