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Knowledge representation of large medical data using XML

Somaraki, Vassiliki and Xu, Zhijie (2016) Knowledge representation of large medical data using XML. In: Proceedings 22nd International Conference on Automation and Computing (ICAC). IEEE. ISBN 9781862181328

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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.

Item Type: Book Chapter
Subjects: Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
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Depositing User: Jing Wang
Date Deposited: 28 Jul 2016 08:18
Last Modified: 14 Dec 2016 11:47
URI: http://eprints.hud.ac.uk/id/eprint/29084

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