Knowledge representation of large medical data using XML

被引：0

作者：

Somaraki, Vassiliki ^{[1
]}

Xu, Zhijie ^{[1
]}

机构：

[1] Univ Huddersfield, Comp & Engn, Huddersfield, W Yorkshire, England

来源：

2016 22ND INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTING (ICAC) | 2016年

关键词：

Medical data; Visualisation; XML; Knowledge Representation; DIABETIC-RETINOPATHY;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：424 / 429

页数：6

共 13 条

[11] Discovering Interesting Trends in Real Medical Data: A Study in Diabetic Retinopathy [J].

Somaraki, Vassiliki ;

Vallati, Mauro ;

McCluskey, Thomas Leo .

PROGRESS IN ARTIFICIAL INTELLIGENCE-BK, 2015, 9273 :134-140

[12]

Somaraki V, 2010, LECT NOTES ARTIF INT, V6171, P418, DOI 10.1007/978-3-642-14400-4_32

[13] A Survey of Interestingness Measures for Association Rules [J].

Zhang, Yuejin ;

Zhang, Lingling ;

Nie, Guangli ;

Shi, Yong .

2009 INTERNATIONAL CONFERENCE ON BUSINESS INTELLIGENCE AND FINANCIAL ENGINEERING, PROCEEDINGS, 2009, :460-463

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