Article | Proceedings from The 16th Scandinavian Conference on Health Informatics 2018, Aalborg, Denmark August 28–29, 2018 | Developing a Bayesian network as a decision support system for evaluating patient with diabetes mellitus admitted to the intensive care unit – a proof of concepts Link�ping University Electronic Press Conference Proceedings
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Title:
Developing a Bayesian network as a decision support system for evaluating patient with diabetes mellitus admitted to the intensive care unit – a proof of concepts
Author:
Rune Sejer Jakobsen: Department of Health Science and Technology, Aalborg University, Aalborg, Denmark Ole Hejlesen: Department of Health Science and Technology, Aalborg University, Aalborg, Denmark Mads Nibe Stausholm: Department of Health Science and Technology, Aalborg University, Aalborg, Denmark Simon Lebech Cichosz: Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
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Full text (pdf)
Year:
2018
Conference:
Proceedings from The 16th Scandinavian Conference on Health Informatics 2018, Aalborg, Denmark August 28–29, 2018
Issue:
151
Article no.:
012
Pages:
70-74
No. of pages:
5
Publication type:
Abstract and Fulltext
Published:
2018-08-24
ISBN:
978-91-7685-213-2
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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Evidence is increasing about an unsatisfying performance from the existing non-disease-specific scoring systems in the intensive care unit (ICU). Evidence is furthermore increasing about differences in the mortality rate between diabetics and non-diabetics dependent on the level of blood glucose (BG), but few scoring systems include these variables in the assessment of the patients. 142,404 ICU admissions were included from the eICU database in the development of an unsupervised trained Bayesian Network (BN). The BN suggested that abnormalities in the level of BG should be associated with differences in the mortality rate between diabetics and non-diabetics. The BN showed promising predictive ability with an AUC on 0.86 for predicting death (sensitivity: 75.06, specificity: 78.40 %). 48.43 % of the length of stays (LOS) were correctly predicted. The results were slightly below the results from the APACHE IV scoring system but showed great ability of risk stratification. The BN showed a potential for predicting the patient outcome and might enable an improved method for risk stratifying the patients admitted to the ICU.

Keywords: Intensive Care Unit, APACHE IV, Mortality, Diabetes Mellitus, Blood Glucose.

Proceedings from The 16th Scandinavian Conference on Health Informatics 2018, Aalborg, Denmark August 28–29, 2018

Author:
Rune Sejer Jakobsen, Ole Hejlesen, Mads Nibe Stausholm, Simon Lebech Cichosz
Title:
Developing a Bayesian network as a decision support system for evaluating patient with diabetes mellitus admitted to the intensive care unit – a proof of concepts
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Proceedings from The 16th Scandinavian Conference on Health Informatics 2018, Aalborg, Denmark August 28–29, 2018

Author:
Rune Sejer Jakobsen, Ole Hejlesen, Mads Nibe Stausholm, Simon Lebech Cichosz
Title:
Developing a Bayesian network as a decision support system for evaluating patient with diabetes mellitus admitted to the intensive care unit – a proof of concepts
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Last updated: 2018-9-11