Knowledge representation of large medical data using XML

Vassiliki Somaraki, Zhijie Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

SOMA uses longitudinal data collected from the Ophthalmology Clinic of the Royal Liverpool University Hospital. Using trend mining (an extension of association rule mining) SOMA links attributes from the data. However the large volume of information at the output makes them difficult to be explored by experts. This paper presents the extension of the SOMA framework which aims to improve the post-processing of the results from experts using a visualisation tool which parse and visualizes the results, which are stored into XML structured files.

Original languageEnglish
Title of host publication2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages423-428
Number of pages6
ISBN (Electronic)9781862181311
DOIs
Publication statusPublished - 20 Oct 2016
Event22nd International Conference on Automation and Computing - Colchester, United Kingdom
Duration: 7 Sep 20168 Sep 2016
Conference number: 22

Conference

Conference22nd International Conference on Automation and Computing
Abbreviated titleICAC 2016
CountryUnited Kingdom
CityColchester
Period7/09/168/09/16

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  • Cite this

    Somaraki, V., & Xu, Z. (2016). Knowledge representation of large medical data using XML. In 2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing (pp. 423-428). [7604956] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IConAC.2016.7604956