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Linked data and online classifications to organise mined patterns in patient data

Jay, Nicolas and d'Aquin, Mathieu (2013). Linked data and online classifications to organise mined patterns in patient data. In: AMIA Annual Symposium, 16-20 November 2013, Washington, DC, USA, pp. 681–690.

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Abstract

In this paper, we investigate the use of web data resources in medicine, especially through medical classifications made available using the principles of Linked Data, to support the interpretation of patterns mined from patient care trajectories. Interpreting such patterns is naturally a challenge for an analyst, as it requires going through large amounts of results and access to sufficient background knowledge. We employ linked data, especially as exposed through the BioPortal system, to create a navigation structure within the patterns obtained form sequential pattern mining. We show how this approach provides a flexible way to explore data about trajectories of diagnoses and treatments according to different medical classifications.

Item Type: Conference or Workshop Item
Copyright Holders: 2013 The Authors
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
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Item ID: 40064
Depositing User: Kay Dave
Date Deposited: 30 Apr 2014 14:54
Last Modified: 04 Oct 2016 15:07
URI: http://oro.open.ac.uk/id/eprint/40064
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