A Multi-Patient Data-Driven Approach to Blood of Glucose Prediction
A Multi-Patient Data-Driven Approach to Blood of Glucose Prediction
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DOI：
10.1109/access.2019.2919184
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Acquaviva, Andrea
中科院分区：
文献类型：
--
作者：
Aliberti, Alessandro;Pupillo, Irene;Acquaviva, Andrea
连续葡萄糖监测系统（CGMS）允许以高采样率测量糖尿病患者的血液透析值，从而产生大量数据。机器学习技术可以有效地使用这些数据来推断血糖浓度的未来值，从而可以早期预防危险的高血糖或低血糖状态，并更好地优化糖尿病治疗。文献中的大多数方法从同一患者的过去样本中学习预测模型，这需要大量的校准并限制了系统的可用性。在本文中，我们研究了在一个大型异质性患者队列的葡萄糖信号上训练的预测模型，然后应用于推断一个全新患者的未来葡萄糖水平值。为了实现这一目的，我们设计并比较了两种不同类型的解决方案，它们分别基于非线性自回归（NAR）神经网络和长短期记忆（LSTM）网络，在许多时间序列预测问题中被证明是成功的。这些解决方案进行了实验比较与三个文献方法，分别基于前馈神经网络（FNNs），自回归（AR）模型，和递归神经网络（RNN）。虽然NAR仅对于短期预测获得了良好的预测精度（即，预测范围在30分钟内），LSTM在短期和长期葡萄糖水平推断（60分钟及以上）方面都获得了非常好的性能，在测量和预测葡萄糖信号之间的相关性以及临床结果方面克服了所有其他方法。
Continuous glucose monitoring systems (CGMSs) allow measuring the blood glycaemic value of a diabetic patient at a high sampling rate, producing a considerable amount of data. These data can be effectively used by machine learning techniques to infer future values of the glycaemic concentration, allowing the early prevention of dangerous hyperglycaemic or hypoglycaemic states and better optimization of the diabetic treatment. Most of the approaches in the literature learn a prediction model from the past samples of the same patient, which needs extensive calibrations and limits the usability of the system. In this paper, we investigate the prediction models trained on glucose signals of a large and heterogeneous cohort of patients and then applied to infer future glucose-level values on a completely new patient. To achieve this purpose, we designed and compared two different types of solutions that were proved successful in many time-series prediction problems based respectively, on non-linear autoregressive (NAR) neural network and on long short-term memory (LSTM) networks. These solutions were experimentally compared with three literature approaches, respectively, based on feed-forward neural networks (FNNs), autoregressive (AR) models, and recurrent neural networks (RNN). While the NAR obtained good prediction accuracy only for short-term predictions (i.e., with prediction horizon within 30 min), the LSTM obtained extremely good performance both for short- and long-term glucose-level inference (60 min and more), overcoming all the other methods in terms of correlation between the measured and the predicted glucose signal and in terms of clinical outcome.