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Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study

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dc.citedby3
dc.contributor.authorAbu-Samah A.en_US
dc.contributor.authorAbdul Razak N.N.en_US
dc.contributor.authorMohamad Suhaimi F.en_US
dc.contributor.authorJamaludin U.K.en_US
dc.contributor.authorChase G.en_US
dc.contributor.authorid56719596600en_US
dc.contributor.authorid37059587300en_US
dc.contributor.authorid36247893200en_US
dc.contributor.authorid55330889600en_US
dc.contributor.authorid35570524900en_US
dc.date.accessioned2023-05-29T07:26:51Z
dc.date.available2023-05-29T07:26:51Z
dc.date.issued2019
dc.descriptionDecision support systems; Forecasting; Intensive care units; Medical informatics; Trees (mathematics); Accurate prediction; Causal Bayesian network; Discretization algorithms; Discretizations; Glycemic control; Intelligent mechanisms; Performance prediction; Variable selection; Bayesian networksen_US
dc.description.abstractHealth informatics in glycemic control is visibly a promising research area. However, this applied science requires more intelligent mechanisms by which user requirements for more accurate prediction can be fulfilled. Such mechanisms must provide very flexible and user friendly procedures to enable complicated decision support functions. This article presents the linking process of per-patient demographic and admission to intensive care unit data with their glycemic control performance using probabilistic causal Bayesian Network models (BNs). Data from two glycemic control protocols are exploited to test the feasibility. The identified steps crucial in building a dependable model are variable selection, state discretization, and structure learning. Different BNs can be generated with more than 83.73% overall precision rate and 93.4% overall calibration index with the combination of these steps. A network with a 95.36% precision was obtained with an equal distance discretization algorithm dataset and Maximum Weight Tree Spanning unsupervised structure learning. The study was the first testing phase in which the results generated by selected data and process is proposed as a benchmark. The resulting network is centred on 'Hypertension' status to predict BG mean and number of measurements as a result of the prediction interest. This co-morbidity is proposed to be considered systematically in the modelling of any glycemic control to optimize its function in the intensive care units. � 2018 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8650206
dc.identifier.doi10.1109/TENCON.2018.8650206
dc.identifier.epage1993
dc.identifier.scopus2-s2.0-85063223965
dc.identifier.spage1988
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85063223965&doi=10.1109%2fTENCON.2018.8650206&partnerID=40&md5=443b2395509eb4ac86adf7ac3848e8c8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24769
dc.identifier.volume2018-October
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Region 10 Annual International Conference, Proceedings/TENCON
dc.titleLinking Bayesian Network and Intensive Care Units Data: A Glycemic Control Studyen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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