Interpretation Method for Continuous Glucose Monitoring with Subsequence Time-Series Clustering...30th Medical Informatics Europe Conference
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 d...
| Publicado en: | Studies in Health Technology & Informatics Vol. 270; pp. 277 - 282 |
|---|---|
| Autores principales: | , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144555247&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144555247 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2020 vid: 270 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 144555247 144555247 144555247 10.3233/SHTI200166 144555247 ppf: 277 ppct: 5 formats: tig: atl: Interpretation Method for Continuous Glucose Monitoring with Subsequence Time-Series Clustering...30th Medical Informatics Europe Conference aug: au: Masaki ONO Takayuki KATSUKI Masaki MAKINO Kyoichi HAIDA Atsushi SUZUKI affil: IBM Research - Tokyo. sug: subj: Data Mining Methods Blood Glucose Monitoring Human Time Series Blood Glucose Cluster Analysis Congresses and Conferences 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. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|