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Prediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application

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dc.contributor.author Tkachenko, Pavlo
dc.contributor.author Kriukova, Galyna
dc.contributor.author Aleksandrova, Marharyta
dc.contributor.author Chertov, Oleg
dc.contributor.author Renard, Eric
dc.contributor.author Pereverzyev, Sergei V.
dc.date.accessioned 2017-06-14T14:14:13Z
dc.date.available 2017-06-14T14:14:13Z
dc.date.issued 2016
dc.identifier.citation Prediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application / Pavlo Tkachenko, Galyna Kriukova, Marharyta Aleksandrova, Oleg Chertov, Eric Renard, Sergei V. Pereverzyev // Computer Methods and Programs in Biomedicine. - 2016. - Vol. 134, October. - P. 179-186. uk
dc.identifier.uri http://ekmair.ukma.edu.ua/handle/123456789/11575
dc.identifier.uri http://doi.org/10.1016/j.cmpb.2016.07.003
dc.description.abstract Background and Objective: Nocturnal hypoglycemia (NH) is common in patients with insulin- treated diabetes. Despite the risk associated with NH, there are only a few methods aiming at the prediction of such events based on intermittent blood glucose monitoring data and none has been validated for clinical use. Here we propose a method of combining several predictors into a new one that will perform at the level of the best involved one, or even outperform all individual candidates. Methods:The idea of the method is to use a recently developed strategy for aggregating ranking algorithms. The method has been calibrated and tested on data extracted from clinical trials, performed in the European FP7-funded project DIAdvisor. Then we have tested the proposed approach on other datasets to show the portability of the method. This feature of the method allows its simple implementation in the form of a diabetic smartphone app. Results:On the considered datasets the proposed approach exhibits good performance in terms of sensitivity, specificity and predictive values. Moreover, the resulting predictor automatically performs at the level of the best involved method or even outperforms it. Conclusion:We propose a strategy for a combination of NH predictors that leads to a method exhibiting a reliable performance and the potential for everyday use by any patient who performs self-monitoring of blood glucose. uk
dc.language.iso en uk
dc.subject Prediction of nocturnal hypoglycemia en
dc.subject Type 1 diabetes en
dc.subject Aggregation en
dc.subject Last before bed measurement en
dc.subject LBGI en
dc.title Prediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application en
dc.type Article uk
dc.status published earlier uk
dc.relation.source Computer Methods and Programs in Biomedicine uk


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