Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study

Benchmarking; Decision support systems; Hospital data processing; Intensive care units; Patient treatment; Trees (mathematics); Blood glucose measurements; Classification precision; Discretization algorithms; Discretizations; Glycemic control; Performance prediction; Structure-learning; Variable sel...

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Main Authors:	Abu-Samah A., Razak N.N.A., Suhaimi F.M., Jamaludin U.K., Chase J.G.
Other Authors:	56719596600
Format:	Article
Published:	Institute of Electronics Engineers of Korea 2023
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Institution:	Universiti Tenaga Nasional

id	my.uniten.dspace-24994
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spelling	my.uniten.dspace-249942023-05-29T15:30:02Z Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study Abu-Samah A. Razak N.N.A. Suhaimi F.M. Jamaludin U.K. Chase J.G. 56719596600 37059587300 36247893200 55330889600 35570524900 Benchmarking; Decision support systems; Hospital data processing; Intensive care units; Patient treatment; Trees (mathematics); Blood glucose measurements; Classification precision; Discretization algorithms; Discretizations; Glycemic control; Performance prediction; Structure-learning; Variable selection; Bayesian networks Personalized treatment in glycemic control (GC) is a visibly promising research area that requires improved mechanisms providing patient-specific procedures to enable complicated decision support. Available per-patient data must be more than written records, and be fully integrated in this personalization process. This article presents a process for relating the intensive care unit patients' demographic and admission data to their GC performance. With this objective, a probabilistic Bayesian network was chosen to provide more personalized decisions. As a case study, average daily blood glucose measurements were chosen as the interest target node in order to weigh GC that provides a reduced nursing workload. To test the idea, data from 482 patients, with nine variables from four Malaysian intensive care units with different controls were exploited. The identified steps crucial in building a dependable model are variable selection, continuous state discretization, and unsupervised structure learning. Using a multi-target node evaluation, a network with 80% mean overall classification precision was obtained with a normalized equal distance discretization algorithm and a maximum weight spanning tree technique. Meanwhile, the interest target node scored 90.39% precision. The results from this study, which are complemented with an evaluation of missing data, are proposed as a benchmark for using Bayesian networks in this type of application. � 2019 Institute of Electronics and Information Engineers. All rights reserved. Final 2023-05-29T07:30:01Z 2023-05-29T07:30:01Z 2019 Article 10.5573/IEIESPC.2019.8.3.202 2-s2.0-85068541334 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068541334&doi=10.5573%2fIEIESPC.2019.8.3.202&partnerID=40&md5=9626ff963dbb29ecf9f895252e07d14d https://irepository.uniten.edu.my/handle/123456789/24994 8 3 202 209 All Open Access, Green Institute of Electronics Engineers of Korea Scopus
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/
description	Benchmarking; Decision support systems; Hospital data processing; Intensive care units; Patient treatment; Trees (mathematics); Blood glucose measurements; Classification precision; Discretization algorithms; Discretizations; Glycemic control; Performance prediction; Structure-learning; Variable selection; Bayesian networks
author2	56719596600
author_facet	56719596600 Abu-Samah A. Razak N.N.A. Suhaimi F.M. Jamaludin U.K. Chase J.G.
format	Article
author	Abu-Samah A. Razak N.N.A. Suhaimi F.M. Jamaludin U.K. Chase J.G.
spellingShingle	Abu-Samah A. Razak N.N.A. Suhaimi F.M. Jamaludin U.K. Chase J.G. Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study
author_sort	Abu-Samah A.
title	Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study
title_short	Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study
title_full	Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study
title_fullStr	Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study
title_full_unstemmed	Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study
title_sort	towards personalized intensive care decision support using a bayesian network: a multicenter glycemic control study
publisher	Institute of Electronics Engineers of Korea
publishDate	2023
_version_	1806426074766114816

Towards personalized intensive care decision support using a Bayesian network: A multicenter glycemic control study

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