A Combined-Predictor Approach to Glycaemia Prediction for Type 1 Diabetes
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064203%3A_____%2F19%3A10394150" target="_blank" >RIV/00064203:_____/19:10394150 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11130/19:10394150 RIV/68407700:21230/19:00321778 RIV/68407700:21460/19:00321778 RIV/68407700:21730/19:00321778
Result on the web
<a href="https://doi.org/10.1007/978-981-10-9023-3_136" target="_blank" >https://doi.org/10.1007/978-981-10-9023-3_136</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-10-9023-3_136" target="_blank" >10.1007/978-981-10-9023-3_136</a>
Alternative languages
Result language
angličtina
Original language name
A Combined-Predictor Approach to Glycaemia Prediction for Type 1 Diabetes
Original language description
Glycaemia prediction plays a vital role in preventing complications related to diabetes mellitus type 1, supporting physicians in their clinical decisions and motivating diabetics to improve their everyday life. Several algorithms, such as mathematical models or neural networks, have been proposed for blood glucose prediction. An approach of combining several glycaemia prediction models is proposed. The main idea of this framework is that the outcome of each prediction model becomes a new feature for a simple regressive model. This approach can be applied to combine any blood glycaemia prediction algorithms. As an example, the proposed method was used to combine an Autoregressive model with exogenous inputs, a Support Vector Regression model and an Extreme Learning Machine for regression model. The multiple-predictor was compared to these three prediction algorithms on the continuous glucose monitoring system and insulin pump readings of one type 1 diabetic patient for one month. The algorithms were evaluated in terms of root-mean-square error and Clarke error-grid analysis for 30, 45 and 60 min prediction horizons.
Czech name
—
Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
30202 - Endocrinology and metabolism (including diabetes, hormones)
Result continuities
Project
<a href="/en/project/NV15-25710A" target="_blank" >NV15-25710A: Individual dynamics of glycaemia excursions identification in diabetic patients to improve self managing procedures influencing insulin dosage</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
World Congress on Medical Physics and Biomedical Engineering 2018, Vol 3
ISBN
978-981-10-9022-6
ISSN
1680-0737
e-ISSN
—
Number of pages
4
Pages from-to
753-756
Publisher name
Springer-Verlag
Place of publication
New York
Event location
Praha
Event date
Jun 3, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000449744300136