Neural Multi-class Classification Approach to Blood Glucose Level Forecasting with Prediction Uncertainty Visualisation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43959339" target="_blank" >RIV/49777513:23520/20:43959339 - isvavai.cz</a>

Result on the web

<a href="http://ceur-ws.org/Vol-2675/" target="_blank" >http://ceur-ws.org/Vol-2675/</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Neural Multi-class Classification Approach to Blood Glucose Level Forecasting with Prediction Uncertainty Visualisation

Original language description

A machine learning-based method for blood glucose level prediction thirty and sixty minutes in advance based on highly multiclass classification (as opposed to the more traditional regression approach) is proposed. An advantage of this approach is the possibility of modelling and visualising the uncertainty of a prediction across the entire range of blood glucose levels without parametric assumptions such as normality. To demonstrate the approach, a long-short term memory-based neural network classifier is used in conjunction with a blood glucose-specific data preprocessing technique (risk domain transform) to train a set of models and generate predictions for the 2018 and 2020 Blood Glucose Level Prediction Competition datasets. Numeric accuracy results are reported along with examples of the uncertainty visualisation possible using this technique.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

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

Article name in the collection

The 5th International Workshop onKnowledge Discovery in Healthcare Data (KDH)

ISBN

—

ISSN

1613-0073

e-ISSN

—

Number of pages

5

Pages from-to

80-84

Publisher name

CEUR Workshop Proceedings (CEUR-WS.org)

Place of publication

Německo

Event location

Santiago de Compostela

Event date

Aug 29, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—