Short-Term Interval Prediction of Glucose with Probabilistic Models

Hajrudin Efendic; Harald Kirchsteiger; Guido Freckmann; Luigi Del Re

doi:10.1089/dia.2014.1515

Short-Term Interval Prediction of Glucose with Probabilistic Models

Hajrudin Efendic, Harald Kirchsteiger, Guido Freckmann, Luigi Del Re

Institute of Design and Control of Mechatronical Systems

Research output: Other contribution

Abstract

Prediction of future glucose using continuous glucose monitoring (CGM) data is an active area of research and many predictors have been proposed. An inherent difficulty is the high variability associated with unknown or immeasurable influence factors. The approach pro-posed here utilizes Gaussian mixture models to predict a range of future glucose levels, tak-ing into consideration their specific probability distribution.

Original language	English
Number of pages	2
Volume	16
DOIs	https://doi.org/10.1089/dia.2014.1515
Publication status	Published - Feb 2014

Fields of science

206002 Electro-medical engineering
207109 Pollutant emission
202 Electrical Engineering, Electronics, Information Engineering
202027 Mechatronics
202034 Control engineering
203027 Internal combustion engines
206001 Biomedical engineering

JKU Focus areas

Mechatronics and Information Processing

Access to Document

10.1089/dia.2014.1515

Cite this

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title = "Short-Term Interval Prediction of Glucose with Probabilistic Models",

abstract = "Prediction of future glucose using continuous glucose monitoring (CGM) data is an active area of research and many predictors have been proposed. An inherent difficulty is the high variability associated with unknown or immeasurable influence factors. The approach pro-posed here utilizes Gaussian mixture models to predict a range of future glucose levels, tak-ing into consideration their specific probability distribution.",

author = "Hajrudin Efendic and Harald Kirchsteiger and Guido Freckmann and \{Del Re\}, Luigi",

year = "2014",

month = feb,

doi = "10.1089/dia.2014.1515",

language = "English",

volume = "16",

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AU - Efendic, Hajrudin

AU - Kirchsteiger, Harald

AU - Freckmann, Guido

AU - Del Re, Luigi

PY - 2014/2

Y1 - 2014/2

N2 - Prediction of future glucose using continuous glucose monitoring (CGM) data is an active area of research and many predictors have been proposed. An inherent difficulty is the high variability associated with unknown or immeasurable influence factors. The approach pro-posed here utilizes Gaussian mixture models to predict a range of future glucose levels, tak-ing into consideration their specific probability distribution.

AB - Prediction of future glucose using continuous glucose monitoring (CGM) data is an active area of research and many predictors have been proposed. An inherent difficulty is the high variability associated with unknown or immeasurable influence factors. The approach pro-posed here utilizes Gaussian mixture models to predict a range of future glucose levels, tak-ing into consideration their specific probability distribution.

U2 - 10.1089/dia.2014.1515

DO - 10.1089/dia.2014.1515

M3 - Other contribution

VL - 16

ER -