Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study
Decision 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 networks
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-24769 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-247692023-05-29T15:26:51Z Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study Abu-Samah A. Abdul Razak N.N. Mohamad Suhaimi F. Jamaludin U.K. Chase G. 56719596600 37059587300 36247893200 55330889600 35570524900 Decision 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 networks Health 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. Final 2023-05-29T07:26:51Z 2023-05-29T07:26:51Z 2019 Conference Paper 10.1109/TENCON.2018.8650206 2-s2.0-85063223965 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063223965&doi=10.1109%2fTENCON.2018.8650206&partnerID=40&md5=443b2395509eb4ac86adf7ac3848e8c8 https://irepository.uniten.edu.my/handle/123456789/24769 2018-October 8650206 1988 1993 Institute of Electrical and Electronics Engineers Inc. 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 |
Decision 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 networks |
| author2 |
56719596600 |
| author_facet |
56719596600 Abu-Samah A. Abdul Razak N.N. Mohamad Suhaimi F. Jamaludin U.K. Chase G. |
| format |
Conference Paper |
| author |
Abu-Samah A. Abdul Razak N.N. Mohamad Suhaimi F. Jamaludin U.K. Chase G. |
| spellingShingle |
Abu-Samah A. Abdul Razak N.N. Mohamad Suhaimi F. Jamaludin U.K. Chase G. Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study |
| author_sort |
Abu-Samah A. |
| title |
Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study |
| title_short |
Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study |
| title_full |
Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study |
| title_fullStr |
Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study |
| title_full_unstemmed |
Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study |
| title_sort |
linking bayesian network and intensive care units data: a glycemic control study |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806427676900065280 |
| score |
13.188404 |