Artificial Intelligence Based Insulin Sensitivity Prediction for Personalized Glycaemic Control in Intensive Care

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dc.contributor.authorBenyó B
dc.contributor.authorPaláncz B
dc.contributor.authorSzlávecz Á
dc.contributor.authorSzabó B
dc.contributor.authorAnane Y
dc.contributor.authorKovács K
dc.contributor.authorChase, Geoff
dc.date.accessioned2021-06-16T21:44:43Z
dc.date.available2021-06-16T21:44:43Z
dc.date.issued2020en
dc.date.updated2021-04-20T06:33:41Z
dc.description.abstractStress-induced hyperglycaemia is a frequent complication in the intensive therapy that can be safely and efficiently treated by using the recently developed model-based tight glycaemic control (TGC) protocols. The most widely applied TGC protocol is the STAR (Stochastic-TARgeted) protocol which uses the insulin sensitivity (SI) for the assessment of the patients state. The patient-specific metabolic variability is managed by the so-called stochastic model allowing the prediction of the 90% confidence interval of the future SI value of the patients. In this paper deep neural network (DNN) based methods (classification DNN and Mixture Density Network) are suggested to implement the patient state prediction. The deep neural networks are trained by using three years of STAR treatment data. The methods are validated by comparing the prediction statistics with the reference data set. The prediction accuracy was also compared with the stochastic model currently used in the clinical practice. The presented results proved the applicability of the neural network based methods for the patient state prediction in the model based clinical treatment. Results suggest that the methods’ prediction accuracy was the same or better than the currently used stochastic model. These results are the initial successful step in the validation process of the proposed methods which will be followed by in-silico simulation trials.en
dc.identifier.citationBenyó B, Paláncz B, Szlávecz Á, Szabó B, Anane Y, Kovács K, Chase JG (2020). Artificial Intelligence Based Insulin Sensitivity Prediction for Personalized Glycaemic Control in Intensive Care. IFAC-PapersOnLine. 53(2). 16335-16340.en
dc.identifier.doihttp://doi.org/10.1016/j.ifacol.2020.12.659
dc.identifier.issn2405-8963
dc.identifier.urihttps://hdl.handle.net/10092/102066
dc.languageen
dc.language.isoen
dc.publisherElsevier BVen
dc.rightsAll rights reserved unless otherwise stateden
dc.rights.urihttp://hdl.handle.net/10092/17651en
dc.subjectmachine learningen
dc.subjectartificial intelligenceen
dc.subjectmixture density networken
dc.subjectdeep neural networken
dc.subjectinsulin sensitivityen
dc.subjecttight glycaemic controlen
dc.subjectintensive careen
dc.subjectSTAR protocolen
dc.subject.anzsrcFields of Research::32 - Biomedical and clinical sciences::3202 - Clinical sciences::320212 - Intensive careen
dc.subject.anzsrcFields of Research::32 - Biomedical and clinical sciences::3202 - Clinical sciences::320208 - Endocrinologyen
dc.subject.anzsrcFields of Research::40 - Engineering::4003 - Biomedical engineering::400306 - Computational physiologyen
dc.subject.anzsrcFields of Research::40 - Engineering::4009 - Electronics, sensors and digital hardware::400909 - Photonic and electro-optical devices, sensors and systems (excl. communications)en
dc.subject.anzsrcFields of Research::46 - Information and computing sciences::4611 - Machine learning::461104 - Neural networksen
dc.subject.anzsrcFields of Research::46 - Information and computing sciences::4611 - Machine learning::461103 - Deep learningen
dc.titleArtificial Intelligence Based Insulin Sensitivity Prediction for Personalized Glycaemic Control in Intensive Careen
dc.typeJournal Articleen
uc.collegeFaculty of Engineering
uc.departmentMechanical Engineering
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