Reducing high-risk glucose forecasting errors by evolving interpretable models for Type 1 diabetes

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43967004" target="_blank" >RIV/49777513:23520/23:43967004 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1568494623000303" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494623000303</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2023.110012" target="_blank" >10.1016/j.asoc.2023.110012</a>

Result language

angličtina

Original language name

Reducing high-risk glucose forecasting errors by evolving interpretable models for Type 1 diabetes

Original language description

Diabetes mellitus is a metabolic disease involving high blood glucose levels that can lead to serious medical consequences. Hence, for diabetic patients the prediction of future glucose levels is essential in the management of the disease. Most of the forecasting approaches in the literature evaluate the effectiveness of glucose predictors only with numerical metrics. These approaches are limited because they evenly treat all the errors without considering their different clinical impact that could involve lethal effects in dangerous situations such as hypo- or hyperglycemia. To overcome such a limitation, this paper aims to devise models for reducing high-risk glucose forecasting errors for Type 1 diabetic patients. For this purpose, we exploit a Grammatical Evolution algorithm to induce personalized and interpretable forecasting glucose models assessed with a novel, composite metric to satisfy both clinical and numerical requirements of the estimated predictions. To assess the effectiveness of the proposed approach, a real-world data set widely used in literature, consisting of data from several patients suffering from Type 1 diabetes, has been adopted. The experimental findings show that the induced models are interpretable and capable of assuring predictions with a good tradeoff between medical quality and numerical accuracy and with remarkable performance in reducing high-risk glucose forecasting errors. Furthermore, their performance is better than or comparable to that of other state-of-the-art methods.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Name of the periodical

Applied Soft Computing

ISSN

1568-4946

e-ISSN

1872-9681

Volume of the periodical

134

Issue of the periodical within the volume

FEB 2023

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

14

Pages from-to

1-14

UT code for WoS article

000967876200001

EID of the result in the Scopus database

2-s2.0-85146147321