Linking Bayesian Network and Intensive Care Units Data

A Glycemic Control Study

Asma Abu-Samah, Normy Norfiza Abdul Razak, Fatanah Mohamad Suhaimi, Ummu Kulthum Jamaludin, Geoffrey Chase

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1988-1993
Number of pages6
ISBN (Electronic)9781538654576
DOIs
Publication statusPublished - 22 Feb 2019
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 28 Oct 201831 Oct 2018

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2018-October
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2018 IEEE Region 10 Conference, TENCON 2018
CountryKorea, Republic of
CityJeju
Period28/10/1831/10/18

Fingerprint

Intensive care units
Bayesian networks
Health
Calibration
Testing

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Abu-Samah, A., Abdul Razak, N. N., Mohamad Suhaimi, F., Jamaludin, U. K., & Chase, G. (2019). Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study. In Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference (pp. 1988-1993). [8650206] (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2018.8650206
Abu-Samah, Asma ; Abdul Razak, Normy Norfiza ; Mohamad Suhaimi, Fatanah ; Jamaludin, Ummu Kulthum ; Chase, Geoffrey. / Linking Bayesian Network and Intensive Care Units Data : A Glycemic Control Study. Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1988-1993 (IEEE Region 10 Annual International Conference, Proceedings/TENCON).
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abstract = "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.",
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Abu-Samah, A, Abdul Razak, NN, Mohamad Suhaimi, F, Jamaludin, UK & Chase, G 2019, Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study. in Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference., 8650206, IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2018-October, Institute of Electrical and Electronics Engineers Inc., pp. 1988-1993, 2018 IEEE Region 10 Conference, TENCON 2018, Jeju, Korea, Republic of, 28/10/18. https://doi.org/10.1109/TENCON.2018.8650206

Linking Bayesian Network and Intensive Care Units Data : A Glycemic Control Study. / Abu-Samah, Asma; Abdul Razak, Normy Norfiza; Mohamad Suhaimi, Fatanah; Jamaludin, Ummu Kulthum; Chase, Geoffrey.

Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1988-1993 8650206 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abu-Samah A, Abdul Razak NN, Mohamad Suhaimi F, Jamaludin UK, Chase G. Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study. In Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1988-1993. 8650206. (IEEE Region 10 Annual International Conference, Proceedings/TENCON). https://doi.org/10.1109/TENCON.2018.8650206