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SOMA: A Proposed Framework for Trend Mining in Large UK Diabetic Retinopathy Temporal Databases

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Research and Development in Intelligent Systems XXVII (SGAI 2010)

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Abstract

In this paper, we present SOMA, a new trend mining framework; and Aretaeus, the associated trend mining algorithm. The proposed framework is able to detect different kinds of trends within longitudinal datasets. The prototype trends are defined mathematically so that they can be mapped onto the temporal patterns. Trends are defined and generated in terms of the frequency of occurrence of pattern changes over time. To evaluate the proposed framework the process was applied to a large collection of medical records, forming part of the diabetic retinopathy screening programme at the Royal Liverpool University Hospital.

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References

  1. Somaraki, V., Broadbent, D., Coenen, F. and Harding, S.: Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy. Proc. 10th Ind. Conf. on Data Mining, Springer LNAI 6171, pp 418-431 (2010).

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, UK, L69 3BX

    Vassiliki Somaraki & Frans Coenen

  2. University of Liverpool, Liverpool, L69 3GA, UK

    Vassiliki Somaraki

  3. Ophthalmology Research Unit, School of Clinical Science, University of Liverpool, Liverpool, L69 3GA, UK

    Simon Harding & Deborah Broadbent

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

    Simon Harding & Deborah Broadbent

Authors
  1. Vassiliki Somaraki
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  2. Simon Harding
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  3. Deborah Broadbent
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  4. Frans Coenen
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Corresponding authors

Correspondence to Vassiliki Somaraki , Simon Harding , Deborah Broadbent or Frans Coenen .

Editor information

Editors and Affiliations

  1. Dept. Computer Science and, Software Engineering, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth, PO1 3HE, United Kingdom

    Max Bramer

  2. School of Computing &, Mathematical Sciences, University of Greenwich, Park Row 30, London, SE10 9LS, United Kingdom

    Miltos Petridis

  3. , Faculty of Technology, De Montford University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Adrian Hopgood

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© 2011 Springer-Verlag London Limited

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Somaraki, V., Harding, S., Broadbent, D., Coenen, F. (2011). SOMA: A Proposed Framework for Trend Mining in Large UK Diabetic Retinopathy Temporal Databases. In: Bramer, M., Petridis, M., Hopgood, A. (eds) Research and Development in Intelligent Systems XXVII. SGAI 2010. Springer, London. https://doi.org/10.1007/978-0-85729-130-1_22

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  • DOI: https://doi.org/10.1007/978-0-85729-130-1_22

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-129-5

  • Online ISBN: 978-0-85729-130-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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