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

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dc.citedby1
dc.contributor.authorAbu-Samah A.en_US
dc.contributor.authorRazak N.N.A.en_US
dc.contributor.authorSuhaimi F.M.en_US
dc.contributor.authorJamaludin U.K.en_US
dc.contributor.authorRalib A.M.en_US
dc.contributor.authorid56719596600en_US
dc.contributor.authorid37059587300en_US
dc.contributor.authorid36247893200en_US
dc.contributor.authorid55330889600en_US
dc.contributor.authorid37031770900en_US
dc.date.accessioned2023-05-29T07:26:45Z
dc.date.available2023-05-29T07:26:45Z
dc.date.issued2019
dc.description.abstractGlycemic 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11113/jt.v81.12721
dc.identifier.epage69
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85062991963
dc.identifier.spage61
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85062991963&doi=10.11113%2fjt.v81.12721&partnerID=40&md5=26647c1c6103200ef6a23bbf11ce83ea
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24759
dc.identifier.volume81
dc.publisherPenerbit UTM Pressen_US
dc.relation.ispartofAll Open Access, Bronze, Green
dc.sourceScopus
dc.sourcetitleJurnal Teknologi
dc.titleProbabilistic glycemic control decision support in ICU: Proof of concept using bayesian networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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