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The Diabino System: Temporal Pattern Mining from Diabetes Healthcare and Daily Self-monitoring Data

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Abstract

In this study, we present an intelligent clinical diabetes management system to support the processes of follow up and treatment of diabetic patients. In addition, temporal pattern mining is proposed as a tool for explaining and predicting the long-term course of the disease. In particular, a fast time-interval pattern mining algorithm is utilized for knowledge discovery from a multivariate dataset concerning not only long-term clinical diabetes data but also daily self-monitoring data.

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Acknowledgement

This work is supported by the research project “Development of an Information Environment for Diabetes Data Analysis and New Knowledge Mining” that has been co-financed by the European Union (European Regional Development Fund—ERDF) and Greek national funds through the Operational Program “THESSALY-MAINLAND GREECE AND EPIRUS-2007–2013” of the National Strategic Reference Framework (NSRF 2007–2013).

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Authors and Affiliations

  1. Unit of Medical Technology and Intelligent Information Systems, Materials Science and Engineering Department, University of Ioannina, 45110, Ioannina, GR, Greece

    Eleni I. Georga, Vasilios C. Protopappas & Dimitrios I. Fotiadis

  2. Institute of Molecular Biology and Biotechnology, Biomedical Research Department, FORTH, University of Ioannina, 45110, Ioannina, GR, Greece

    Dimitrios I. Fotiadis

  3. Department of Endocrinology, Hatzikosta General Hospital, 45445, Ioannina, GR, Greece

    Eleni Arvaniti

Authors
  1. Eleni I. Georga
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  2. Vasilios C. Protopappas
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  3. Eleni Arvaniti
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  4. Dimitrios I. Fotiadis
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Corresponding author

Correspondence to Dimitrios I. Fotiadis .

Editor information

Editors and Affiliations

  1. The Chinese University of Hong Kong, Hong Kong, China

    Yuan-Ting Zhang

  2. Universidade De Coimbra, Coimbra, Portugal

    Paulo Carvalho

  3. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Ratko Magjarevic

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Georga, E.I., Protopappas, V.C., Arvaniti, E., Fotiadis, D.I. (2019). The Diabino System: Temporal Pattern Mining from Diabetes Healthcare and Daily Self-monitoring Data. In: Zhang, YT., Carvalho, P., Magjarevic, R. (eds) International Conference on Biomedical and Health Informatics. ICBHI 2015. IFMBE Proceedings, vol 64. Springer, Singapore. https://doi.org/10.1007/978-981-10-4505-9_10

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  • DOI: https://doi.org/10.1007/978-981-10-4505-9_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4504-2

  • Online ISBN: 978-981-10-4505-9

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