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

[1]

[Anonymous], KNOWLEDGE DISCOVERY

[2]

[Anonymous], 2012, DATA PREPROCESSING

[3]

DeRose S., 1999, XML TEI COMPUTERS HU, P11

[4]

Jitao Zhao, 2010, 2010 International Conference on Future Information Technology and Management Engineering (FITME 2010), P163, DOI 10.1109/FITME.2010.5654718

[5]

Kotsiantis S., 2006, GESTS International Transactions on Computer Science and Engineering, V32, P47

[6]

Lenca P., 2007, QUALITY MEASURES DAT, V43, P51, DOI DOI 10.1007/978-3-540-44918-8_3

[7]

Liu H, 2002, DCABES 2002, PROCEEDING, P393

[8]

Piatetsky-Shapiro G., 1991, Knowledge discovery in databases, P229

[9]

Singer D., 2003, APPL LONGITUDINAL DA

[10]

Somaraki V., 2013, THESIS

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