A Combined-Predictor Approach to Glycaemia Prediction for Type 1 Diabetes

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11130/19:10394150 RIV/68407700:21230/19:00321778 RIV/68407700:21460/19:00321778 RIV/68407700:21730/19:00321778

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A Combined-Predictor Approach to Glycaemia Prediction for Type 1 Diabetes

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A Combined-Predictor Approach to Glycaemia Prediction for Type 1 Diabetes

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30202 - Endocrinology and metabolism (including diabetes, hormones)

Projekt

<a href="/cs/project/NV15-25710A" target="_blank" >NV15-25710A: Identifikace individuální dynamiky glykemických exkurzí u pacientů s diabetem pro zlepšení rozhodovacích postupů ovlivňujících dávkování inzulínu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

World Congress on Medical Physics and Biomedical Engineering 2018, Vol 3

ISBN

978-981-10-9022-6

ISSN

1680-0737

e-ISSN

—

Počet stran výsledku

4

Strana od-do

753-756

Název nakladatele

Springer-Verlag

Místo vydání

New York

Místo konání akce

Praha

Datum konání akce

3. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000449744300136