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Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks

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dc.contributor.authorShah N.N.H.en_US
dc.contributor.authorRazak A.A.en_US
dc.contributor.authorRazak N.N.en_US
dc.contributor.authorRamasamy A.K.en_US
dc.contributor.authorAbu-Samah A.en_US
dc.contributor.authorHasan M.S.en_US
dc.contributor.authorid7401823793en_US
dc.contributor.authorid56960052400en_US
dc.contributor.authorid37059587300en_US
dc.contributor.authorid16023154400en_US
dc.contributor.authorid56719596600en_US
dc.contributor.authorid54083209700en_US
dc.date.accessioned2023-05-29T09:10:16Z
dc.date.available2023-05-29T09:10:16Z
dc.date.issued2021
dc.descriptionBayesian networks; Blood pressure; Classification (of information); Creatinine; Failure (mechanical); Machine learning; Bayesia n networks; Comorbidities; Data discretization; Failure assessment; Machine-learning; Model dynamics; Organ failure; Renal failure; Sequential organ failure assessment score; Variables selections; Intensive care unitsen_US
dc.description.abstractRenal failure in the intensive care unit (ICU) is associated with high morbidity and mortality. The Sequential Organ Failure Assessment (SOFA) score is applied in the ICU to track the progression of organ dysfunction. The renal component of the SOFA score employed serum creatinine and urine output to define the stage of its dysfunction. This study aims to explore the relationship between commonly available variables in the ICU together patients' gender and comorbidities to renal failure employing Bayesian Network. The process of building Bayesian Networks involved variable selection, data discretization, and aggregation before structural learning method. The dataset was discretized using equal distance technique into 3 intervals before it was fed into unsupervised structural classification learning techniques. The highest overall precision of 85.1 % was achieved using the unsupervised learning Taboo Order Bayesian Network. Other than creatinine, heart rate, systolic blood pressure, temperature, diabetes mellitus, and hypertension are directly connected with renal failure in this Bayesian Network. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICSET53708.2021.9612523
dc.identifier.epage138
dc.identifier.scopus2-s2.0-85123342305
dc.identifier.spage134
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123342305&doi=10.1109%2fICSET53708.2021.9612523&partnerID=40&md5=7f62d948efe2eed2f626817ec4d247d0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26418
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2021 IEEE 11th International Conference on System Engineering and Technology, ICSET 2021 - Proceedings
dc.titleModeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networksen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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