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Discovering Interesting Trends in Real Medical Data: A study in Diabetic Retinopathy

Somaraki, Vassiliki, Vallati, Mauro and McCluskey, Lee (2015) Discovering Interesting Trends in Real Medical Data: A study in Diabetic Retinopathy. In: Progress in Artificial Intelligence : 17th Portuguese Conference on Artificial Intelligence, EPIA 2015 Proceedings. Lecture Notes in Computer Science, 9273 . Springer, pp. 134-140. ISBN 978-3-319-23484-7

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Abstract

In this work we present SOMA: a Trend Mining framework, based on longitudinal data analysis, that is able to measure the interestingness of the produced trends in large noisy medical databases. Medical longitudinal data typically plots the progress of some medical condition, thus implicitly contains a large number of trends. The approach has been evaluated on a large collection of medical records, forming part of the diabetic retinopathy screening programme at the Royal Liverpool University Hospital, UK.

Item Type: Book Chapter
Additional Information: 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Coimbra, Portugal, September 8-11, 2015. Proceedings
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RE Ophthalmology
Schools: School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge

School of Computing and Engineering > High-Performance Intelligent Computing > Visualisation, Interaction and Vision
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Depositing User: Vassiliki Somaraki
Date Deposited: 03 Aug 2015 14:52
Last Modified: 28 Nov 2015 17:04
URI: http://eprints.hud.ac.uk/id/eprint/24826

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