Irregularly measured time series are common in many of the applied settings
in which time series modelling is a key statistical tool, including medicine.
This provides challenges in model choice, often necessitating imputation or
similar strategies. Continuous time autoregressive recurrent neural networks
(CTRNNs) are a deep learning model that account for irregular observations
through incorporating continuous evolution of the hidden states between
observations. This is achieved using a neural ordinary differential equation
(ODE) or neural flow layer. In this manuscript, we give an overview of these
models, including the varying architectures that have been proposed to account
for issues such as ongoing medical interventions. Further, we demonstrate the
application of these models to probabilistic forecasting of blood glucose in a
critical care setting using electronic medical record and simulated data. The
experiments confirm that addition of a neural ODE or neural flow layer
generally improves the performance of autoregressive recurrent neural networks
in the irregular measurement setting. However, several CTRNN architecture are
outperformed by an autoregressive gradient boosted tree model (Catboost), with
only a long short-term memory (LSTM) and neural ODE based architecture
(ODE-LSTM) achieving comparable performance on probabilistic forecasting
metrics such as the continuous ranked probability score (ODE-LSTM:
0.118$\pm$0.001; Catboost: 0.118$\pm$0.001), ignorance score (0.152$\pm$0.008;
0.149$\pm$0.002) and interval score (175$\pm$1; 176$\pm$1).

本文介绍了在不规则测量环境中，利用神经普通微分方程或神经流层来提高自回归递归神经网络性能的不同 CTRNN 体系结构，研究表明，在血糖概率预测方面，只有 LSTM 和 ODE-LSTM 体系结构与渐进提升树模型具有相当的性能。