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

  • Eleni I. Georga
  • Vasilios C. Protopappas
  • Eleni Arvaniti
  • Dimitrios I. Fotiadis
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 64)


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.


Diabetes management Temporal pattern mining 



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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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Eleni I. Georga
    • 1
  • Vasilios C. Protopappas
    • 1
  • Eleni Arvaniti
    • 3
  • Dimitrios I. Fotiadis
    • 1
    • 2
  1. 1.Unit of Medical Technology and Intelligent Information Systems, Materials Science and Engineering DepartmentUniversity of IoanninaIoanninaGreece
  2. 2.Institute of Molecular Biology and Biotechnology, Biomedical Research Department, FORTHUniversity of IoanninaIoanninaGreece
  3. 3.Department of EndocrinologyHatzikosta General HospitalIoanninaGreece

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