文章基本信息

标题：Comparison of Gated Recurrent Unit vs. Mixture Density Network in Insulin Sensitivity Prediction
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作者：Bálint Szabó ; Ákos Szlávecz ; Béla Paláncz 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：15
页码：180-185
DOI：10.1016/j.ifacol.2022.07.628
语种：English
出版社：Elsevier
摘要：AbstractThere are several methods for the prediction of future values of a time series, or in general, for the probability density prediction problem. In this paper two neural network prediction models are compared: Mixture Density Network (MDN) and Gated Recurrent Unit (GRU) in the prediction of the insulin sensitivity (SI) of a patient under intensive care.The basic difference between the two methods is that while MDN is a stateless network model the GRU is a stateful model. In the study, both of the models predict the probability density function of the future SI value, but the MDN considers the physiological process like a Markov-chain: the future SI value depends lowly on the previous value. In contrast, the GRU model attempts to model the longer-term dependencies between subsequent SI values in its internal states and improve the prediction accuracy based on the information derived from the previous SI values.In the study we have found that the GRU implemented stateful prediction improved the prediction accuracy by 10 percent, showing the prediction power of the GRU model in physiological problems and proving the existence of longer-term dependencies in the SI time series. These outcomes confirm previously published results showing that the insulin sensitivity prediction can be improved considering the longer-term history of the patients.
关键词：KeywordsInsulin sensitivityTime series predictionMachine learningNeural networksGated Recurrent UnitMixture Density NetworkGaussian distributionsProcess control