TY - GEN
T1 - Interpretation method for continuous glucose monitoring with subsequence time-series clustering
AU - Ono, Masaki
AU - Katsuki, Takayuki
AU - Makino, Masaki
AU - Haida, Kyoichi
AU - Suzuki, Atsushi
N1 - Publisher Copyright:
© 2020 European Federation for Medical Informatics (EFMI) and IOS Press.
PY - 2020/6/16
Y1 - 2020/6/16
N2 - 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.
AB - 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.
KW - Continuous glucose monitoring
KW - Subsequence time-series clustering
UR - https://www.scopus.com/pages/publications/85086907067
UR - https://www.scopus.com/pages/publications/85086907067#tab=citedBy
U2 - 10.3233/SHTI200166
DO - 10.3233/SHTI200166
M3 - Conference contribution
C2 - 32570390
AN - SCOPUS:85086907067
T3 - Studies in Health Technology and Informatics
SP - 277
EP - 281
BT - Digital Personalized Health and Medicine - Proceedings of MIE 2020
A2 - Pape-Haugaard, Louise B.
A2 - Lovis, Christian
A2 - Madsen, Inge Cort
A2 - Weber, Patrick
A2 - Nielsen, Per Hostrup
A2 - Scott, Philip
PB - IOS Press
T2 - 30th Medical Informatics Europe Conference, MIE 2020
Y2 - 28 April 2020 through 1 May 2020
ER -