Botwey, Ransford Henry; Daskalaki, Eleni; Diem, Peter; Mougiakakou, Stavroula (August 2014). Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events. IEEE Engineering in Medicine and Biology Society conference proceedings, 2014, pp. 4843-4846. IEEE Service Center 10.1109/EMBC.2014.6944708
![[img]](https://boris.unibe.ch/style/images/fileicons/text.png) |
Text
06944708.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (827kB) | Request a copy |
Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.
Item Type: |
Conference or Workshop Item (Paper) |
|---|---|
Division/Institute: |
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Health and Nutrition 04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition |
UniBE Contributor: |
Botwey, Ransford Henry, Daskalaki, Eleni, Diem, Peter, Mougiakakou, Stavroula |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1557-170X |
Publisher: |
IEEE Service Center |
Language: |
English |
Submitter: |
Stavroula Mougiakakou |
Date Deposited: |
13 May 2015 12:09 |
Last Modified: |
05 Dec 2022 14:46 |
Publisher DOI: |
10.1109/EMBC.2014.6944708 |
PubMed ID: |
25571076 |
BORIS DOI: |
10.7892/boris.68162 |
URI: |
https://boris.unibe.ch/id/eprint/68162 |
Actions (login required)
 |
Edit item |