Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30202 - Endocrinology and metabolism (including diabetes, hormones)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

11

Strana od-do

117-127

Název nakladatele

Springer, Cham

Místo vydání

—

Místo konání akce

Salt Lake City

Datum konání akce

9. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001295129500013