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Probabilistic glycemic control decision support in ICU: Proof of concept using bayesian network

Glycemic control in intensive care patients is complex in terms of patients’ response to care and treatment. The variability and the search for improved insulin therapy outcomes have led to the use of human physiology model based on per-patient metabolic condition to provide personalized automated r...

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Main Authors: Abu-Samah, A., Razak, N.N.A., Suhaimi, F.M., Jamaludin, U.K., Ralib, A.M.
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-131172020-03-12T04:25:34Z Probabilistic glycemic control decision support in ICU: Proof of concept using bayesian network Abu-Samah, A. Razak, N.N.A. Suhaimi, F.M. Jamaludin, U.K. Ralib, A.M. Glycemic control in intensive care patients is complex in terms of patients’ response to care and treatment. The variability and the search for improved insulin therapy outcomes have led to the use of human physiology model based on per-patient metabolic condition to provide personalized automated recommendations. One of the most promising solutions for this is the STAR protocol, which is based on a clinically validated insulin-nutrition-glucose physiological model. However, this approach does not consider demographical background such as age, weight, height, and ethnicity. This article presents the extension to intensive care personalized solution by integrating per-patient demographical, and upon admission information to intensive care conditions to automate decision support for clinical staff. In this context, a virtual study was conducted on 210 retrospectives intensive care patients’ data. To provide a ground, the integration concept is presented roughly, but the details are given in terms of a proof of concept using Bayesian Network, linking the admission background and performance of the STAR control. The proof of concept shows 71.43% and 73.90% overall inference precision, and reliability, respectively, on the test dataset. With more data, improved Bayesian Network is believed to be reproduced. These results, nevertheless, points at the feasibility of the network to act as an effective classifier using intensive care units data, and glycemic control performance to be the basis of a probabilistic, personalized, and automated decision support in the intensive care units. © 2019 Penerbit UTM Press. All rights reserved. 2020-02-03T03:30:29Z 2020-02-03T03:30:29Z 2019 Article 10.11113/jt.v81.12721 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description Glycemic control in intensive care patients is complex in terms of patients’ response to care and treatment. The variability and the search for improved insulin therapy outcomes have led to the use of human physiology model based on per-patient metabolic condition to provide personalized automated recommendations. One of the most promising solutions for this is the STAR protocol, which is based on a clinically validated insulin-nutrition-glucose physiological model. However, this approach does not consider demographical background such as age, weight, height, and ethnicity. This article presents the extension to intensive care personalized solution by integrating per-patient demographical, and upon admission information to intensive care conditions to automate decision support for clinical staff. In this context, a virtual study was conducted on 210 retrospectives intensive care patients’ data. To provide a ground, the integration concept is presented roughly, but the details are given in terms of a proof of concept using Bayesian Network, linking the admission background and performance of the STAR control. The proof of concept shows 71.43% and 73.90% overall inference precision, and reliability, respectively, on the test dataset. With more data, improved Bayesian Network is believed to be reproduced. These results, nevertheless, points at the feasibility of the network to act as an effective classifier using intensive care units data, and glycemic control performance to be the basis of a probabilistic, personalized, and automated decision support in the intensive care units. © 2019 Penerbit UTM Press. All rights reserved.
format Article
author Abu-Samah, A.
Razak, N.N.A.
Suhaimi, F.M.
Jamaludin, U.K.
Ralib, A.M.
spellingShingle Abu-Samah, A.
Razak, N.N.A.
Suhaimi, F.M.
Jamaludin, U.K.
Ralib, A.M.
Probabilistic glycemic control decision support in ICU: Proof of concept using bayesian network
author_facet Abu-Samah, A.
Razak, N.N.A.
Suhaimi, F.M.
Jamaludin, U.K.
Ralib, A.M.
author_sort Abu-Samah, A.
title Probabilistic glycemic control decision support in ICU: Proof of concept using bayesian network
title_short Probabilistic glycemic control decision support in ICU: Proof of concept using bayesian network
title_full Probabilistic glycemic control decision support in ICU: Proof of concept using bayesian network
title_fullStr Probabilistic glycemic control decision support in ICU: Proof of concept using bayesian network
title_full_unstemmed Probabilistic glycemic control decision support in ICU: Proof of concept using bayesian network
title_sort probabilistic glycemic control decision support in icu: proof of concept using bayesian network
publishDate 2020
_version_ 1662758817255915520
score 13.211869

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