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

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Progress in Artificial Intelligence (EPIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9273))

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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.

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Author information

Authors and Affiliations

  1. School of Computing and Engineering, University of Huddersfield, Huddersfield, UK

    Vassiliki Somaraki, Mauro Vallati & Thomas Leo McCluskey

  2. St. Paul’s Eye Unit, Royal Liverpool University Hospital, Liverpool, L7 8XP, UK

    Vassiliki Somaraki

Authors
  1. Vassiliki Somaraki
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  2. Mauro Vallati
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  3. Thomas Leo McCluskey
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Corresponding author

Correspondence to Vassiliki Somaraki .

Editor information

Editors and Affiliations

  1. ISEC - Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Coimbra, Portugal

    Francisco Pereira

  2. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

  4. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Amílcar Cardoso

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Somaraki, V., Vallati, M., McCluskey, T.L. (2015). Discovering Interesting Trends in Real Medical Data: A Study in Diabetic Retinopathy. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_15

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  • DOI: https://doi.org/10.1007/978-3-319-23485-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23484-7

  • Online ISBN: 978-3-319-23485-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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