Prediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application. Issue 134 (October 2016)

• Record Type: Journal Article
• Authors: Tkachenko, Pavlo; Kriukova, Galyna; Aleksandrova, Marharyta; Chertov, Oleg; Renard, Eric; Pereverzyev, Sergei V.
• Is Part Of: Computer methods and programs in biomedicine. Issue 134(2016)
• Page Start: 179
• Page End: 186
• Publication Date: 2016-10
• Subjects: Prediction of nocturnal hypoglycemia -- Type 1 diabetes -- Aggregation -- Last before bed measurement -- LBGI; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.07.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A method of combining predictors into a new one that performs at the level of the best involved one, or outperform all candidates. Portability of the method. This feature of the method allows its simple implementation in form of a diabetic Smartphone app. The potential for everyday use by any patient who performs self-monitoring of blood glucose. The idea is based on the linear functional strategy for regularized ranking. 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.