Knowledge representation of large medical data using XML

Vassiliki Somaraki; Zhijie Xu

doi:10.1109/IConAC.2016.7604956

Knowledge representation of large medical data using XML

Vassiliki Somaraki, Zhijie Xu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	423-428
Number of pages	6
ISBN (Electronic)	9781862181311
DOIs	https://doi.org/10.1109/IConAC.2016.7604956
Publication status	Published - 20 Oct 2016
Event	22nd International Conference on Automation and Computing - Colchester, United Kingdom Duration: 7 Sep 2016 → 8 Sep 2016 Conference number: 22

Conference

Conference	22nd International Conference on Automation and Computing
Abbreviated title	ICAC 2016
Country/Territory	United Kingdom
City	Colchester
Period	7/09/16 → 8/09/16

Access to Document

10.1109/IConAC.2016.7604956Licence: Other

http://eprints.hud.ac.uk/id/eprint/29084/Licence: Unspecified

Cite this

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title = "Knowledge representation of large medical data using XML",

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.",

keywords = "Knowledge Representation, Medical data, Visualisation, XML",

author = "Vassiliki Somaraki and Zhijie Xu",

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doi = "10.1109/IConAC.2016.7604956",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "22nd International Conference on Automation and Computing, ICAC 2016 ; Conference date: 07-09-2016 Through 08-09-2016",

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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., 7604956, Institute of Electrical and Electronics Engineers Inc., pp. 423-428, 22nd International Conference on Automation and Computing, Colchester, United Kingdom, 7/09/16. https://doi.org/10.1109/IConAC.2016.7604956

Knowledge representation of large medical data using XML. / Somaraki, Vassiliki; Xu, Zhijie.

2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing. Institute of Electrical and Electronics Engineers Inc., 2016. p. 423-428 7604956.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

KW - Knowledge Representation

KW - Medical data

KW - Visualisation

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DO - 10.1109/IConAC.2016.7604956

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BT - 2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 7 September 2016 through 8 September 2016

ER -

Knowledge representation of large medical data using XML

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