Modeling Glucose Homeostasis and Insulin Dosing in an Intensive Care Unit using Dynamic Bayesian Networks

Adequate control of serum glucose in critically ill patients is a complex problem requiring continuous monitoring and intervention, which have a direct effect on clinical outcomes. Understanding temporal relationships can help to improve our knowledge of complex disease processes and their response...

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Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2010-11, Vol.2010, p.532-536
Main Authors: Nachimuthu, Senthil K, Wong, Anthony, Haug, Peter J
Format: Article
Language:English
Subjects:
Bayes Theorem
Blood Glucose
Critical Illness
Glucose
Homeostasis
Humans
Insulin - administration & dosage
Intensive Care Units
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Summary:Adequate control of serum glucose in critically ill patients is a complex problem requiring continuous monitoring and intervention, which have a direct effect on clinical outcomes. Understanding temporal relationships can help to improve our knowledge of complex disease processes and their response to treatment. We discuss a Dynamic Bayesian Network (DBN) model that we created using the open-source Projeny toolkit to represent various clinical variables and the temporal and atemporal relationships underlying insulin and glucose homeostasis. We evaluated this model by comparing the DBN model's insulin dose predictions against those of a rule-based protocol (eProtocol-insulin) currently used in the ICU. The results suggest that the DBN model's predictions are as effective as or better than those of the rule-based protocol. The limitations of our methods are discussed, with a brief note on their generalizability.
ISSN:1559-4076