Interpretation method for continuous glucose monitoring with subsequence time-series clustering

Masaki Ono, Takayuki Katsuki, Masaki Makino, Kyoichi Haida, Atsushi Suzuki

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


We propose mini-batch top-n k-medoids to sequential pattern mining to improve CGM interpretation. Mecical workers can treat specific patient groups better by understanding the time series variation of blood glucose results. For 10 years, continuous glucose monitoring (CGM) has provided time-series data of blood glucose thanks to the invention of devices with low measurement errors. We conducted two experiments. In the first experiment, we evaluated the proposed method with a manually created dataset and confirmed that the method provides more accurate patterns than other clustering methods. In the second experiment, we applied the proposed method to a CGM dataset consisting of real data from 163 patients. We created two labels based on blood glucose (BG) statistics and found patterns that correlated with a specific label in each case.

Original languageEnglish
Title of host publicationDigital Personalized Health and Medicine - Proceedings of MIE 2020
EditorsLouise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
PublisherIOS Press
Number of pages5
ISBN (Electronic)9781643680828
Publication statusPublished - 16-06-2020
Externally publishedYes
Event30th Medical Informatics Europe Conference, MIE 2020 - Geneva, Switzerland
Duration: 28-04-202001-05-2020

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Conference30th Medical Informatics Europe Conference, MIE 2020

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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