Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00378416" target="_blank" >RIV/68407700:21240/24:00378416 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-66538-7_13" target="_blank" >https://doi.org/10.1007/978-3-031-66538-7_13</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-66538-7_13" target="_blank" >10.1007/978-3-031-66538-7_13</a>
Alternative languages
Result language
angličtina
Original language name
Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model
Original language description
Type 1 diabetes disrupts normal blood glucose regulation due to the destruction of insulin-producing cells, necessitating insulin therapy through injections or insulin pumps. Consumer devices can forecast blood glucose levels by leveraging data from blood glucose sensors and other sources. Such predictions are valuable for informing patients about their blood glucose trajectory and supporting various downstream applications. Numerous machine-learning models have been explored for blood glucose prediction. This study introduces a novel application of Legendre Memory Units for blood glucose prediction. Employing a multivariate time series, predictions are made with 30-minute and 60-minute horizons. The proposed model is comparable with state-of-the-art models on the OhioT1DM dataset, encompassing eight weeks of data from 12 distinct patients.
Czech name
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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
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Intelligence in Medicine, 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part I
ISBN
978-3-031-66538-7
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
11
Pages from-to
117-127
Publisher name
Springer, Cham
Place of publication
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Event location
Salt Lake City
Event date
Jul 9, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001295129500013