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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Institute of Electronics Engineers of Korea
2023
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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 |
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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 |
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56719596600 Abu-Samah A. Razak N.N.A. Suhaimi F.M. Jamaludin U.K. Chase J.G. |
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Article |
| author |
Abu-Samah A. Razak N.N.A. Suhaimi F.M. Jamaludin U.K. Chase J.G. |
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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 |
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13.188404 |