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

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルDigital Personalized Health and Medicine - Proceedings of MIE 2020
編集者Louise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
出版社IOS Press
ページ277-281
ページ数5
ISBN(電子版)9781643680828
DOI
出版ステータスPublished - 16-06-2020
外部発表はい
イベント30th Medical Informatics Europe Conference, MIE 2020 - Geneva, Switzerland
継続期間: 28-04-202001-05-2020

出版物シリーズ

名前Studies in Health Technology and Informatics
270
ISSN(印刷版)0926-9630
ISSN(電子版)1879-8365

Conference

Conference30th Medical Informatics Europe Conference, MIE 2020
国/地域Switzerland
CityGeneva
Period28-04-2001-05-20

All Science Journal Classification (ASJC) codes

  • 生体医工学
  • 健康情報学
  • 健康情報管理

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