Improving risk-stratification of Diabetes complications using temporal data mining

Lucia Sacchi, Arianna Dagliati, Daniele Segagni, Paola Leporati, Luca Chiovato, Riccardo Bellazzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To understand which factor trigger worsened disease control is a crucial step in Type 2 Diabetes (T2D) patient management. The MOSAIC project, funded by the European Commission under the FP7 program, has been designed to integrate heterogeneous data sources and provide decision support in chronic T2D management through patients' continuous stratification. In this work we show how temporal data mining can be fruitfully exploited to improve risk stratification. In particular, we exploit administrative data on drug purchases to divide patients in meaningful groups. The detection of drug consumption patterns allows stratifying the population on the basis of subjects' purchasing attitude. Merging these findings with clinical values indicates the relevance of the applied methods while showing significant differences in the identified groups. This extensive approach emphasized the exploitation of administrative data to identify patterns able to explain clinical conditions.

Original languageEnglish
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2131-2134
Number of pages4
Volume2015-November
ISBN (Print)9781424492718
DOIs
Publication statusPublished - Nov 4 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015Aug 29 2015

Other

Other37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
CountryItaly
CityMilan
Period8/25/158/29/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Sacchi, L., Dagliati, A., Segagni, D., Leporati, P., Chiovato, L., & Bellazzi, R. (2015). Improving risk-stratification of Diabetes complications using temporal data mining. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Vol. 2015-November, pp. 2131-2134). [7318810] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7318810