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., Normy Norfiza, A. Razak, Fatanah, M. Suhaimi, Azrina, Md Ralib, Ummu Kulthum, Jamaludin
Format: Article
Language:English
Published: Penerbit UTM Press 2019
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Online Access:http://umpir.ump.edu.my/id/eprint/24893/
http://umpir.ump.edu.my/id/eprint/24893/
http://umpir.ump.edu.my/id/eprint/24893/
http://umpir.ump.edu.my/id/eprint/24893/1/Probabilistic%20glycemic%20control%20decision%20support%20in%20ICU.pdf
id ump-24893
recordtype eprints
spelling ump-248932019-10-11T08:14:55Z http://umpir.ump.edu.my/id/eprint/24893/ Probabilistic glycemic control decision support in ICU : proof of concept using bayesian network Abu-Samah, A. Normy Norfiza, A. Razak Fatanah, M. Suhaimi Azrina, Md Ralib Ummu Kulthum, Jamaludin QA Mathematics R Medicine (General) RC Internal medicine 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 Penerbit UTM Press 2019-03 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24893/1/Probabilistic%20glycemic%20control%20decision%20support%20in%20ICU.pdf Abu-Samah, A. and Normy Norfiza, A. Razak and Fatanah, M. Suhaimi and Azrina, Md Ralib and Ummu Kulthum, Jamaludin (2019) Probabilistic glycemic control decision support in ICU : proof of concept using bayesian network. Jurnal Teknologi (Sciences and Engineering), 81 (2). pp. 61-69. ISSN 0127-9696 (print); 2180-3722 (online) https://doi.org/10.11113/jt.v81.12721 https://doi.org/10.11113/jt.v81.12721
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA Mathematics
R Medicine (General)
RC Internal medicine
spellingShingle QA Mathematics
R Medicine (General)
RC Internal medicine
Abu-Samah, A.
Normy Norfiza, A. Razak
Fatanah, M. Suhaimi
Azrina, Md Ralib
Ummu Kulthum, Jamaludin
Probabilistic glycemic control decision support in ICU : proof of concept using bayesian network
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
format Article
author Abu-Samah, A.
Normy Norfiza, A. Razak
Fatanah, M. Suhaimi
Azrina, Md Ralib
Ummu Kulthum, Jamaludin
author_facet Abu-Samah, A.
Normy Norfiza, A. Razak
Fatanah, M. Suhaimi
Azrina, Md Ralib
Ummu Kulthum, Jamaludin
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
publisher Penerbit UTM Press
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/24893/
http://umpir.ump.edu.my/id/eprint/24893/
http://umpir.ump.edu.my/id/eprint/24893/
http://umpir.ump.edu.my/id/eprint/24893/1/Probabilistic%20glycemic%20control%20decision%20support%20in%20ICU.pdf
first_indexed 2023-09-18T22:37:55Z
last_indexed 2023-09-18T22:37:55Z
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