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dc.contributor.authorMayo, Michaelen_NZ
dc.contributor.authorChepulis, Lynne Merranen_NZ
dc.contributor.authorPaul, Ryan G.en_NZ
dc.coverage.spatialUnited Statesen_NZ
dc.date.accessioned2020-02-20T01:50:41Z
dc.date.available2019en_NZ
dc.date.available2020-02-20T01:50:41Z
dc.date.issued2019en_NZ
dc.identifier.citationMayo, M., Chepulis, L., & Paul, R. (2019). Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning. PLoS One, 14(12), e0225613. https://doi.org/10.1371/journal.pone.0225613en
dc.identifier.urihttps://hdl.handle.net/10289/13448
dc.description.abstractTechniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hypoglycemia alarms) and other forecasts can be generated. It is shown that two factors must be considered when selecting the best machine learning technique for blood glucose level regression: (i) the regression model performance metrics being used to select the model, and (ii) the preprocessing techniques required to account for the imbalanced time spent by patients in different portions of the glycemic range. Using standard benchmark data, it is demonstrated that different regression model/preprocessing technique combinations exhibit different accuracies depending on the glycemic subrange under consideration. Therefore technique selection depends on the type of alert required. Specific findings are that a linear Support Vector Regression-based model, trained with normal as well as polynomial features, is best for blood glucose level forecasting in the normal and hyperglycemic ranges while a Multilayer Perceptron trained on oversampled data is ideal for predictions in the hypoglycemic range.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoengen_NZ
dc.rights© 2019 Mayo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.titleGlycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning.en_NZ
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pone.0225613en_NZ
dc.relation.isPartOfPLoS Oneen_NZ
pubs.begin-pagee0225613
pubs.elements-id249933
pubs.issue12en_NZ
pubs.publication-statusPublished onlineen_NZ
pubs.volume14en_NZ
dc.identifier.eissn1932-6203en_NZ


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