标题： 一种用于时间序列预处理和血糖水平预测的多任务变分自编码器 显示英文标题

标题： A Multitask VAE for Time Series Preprocessing and Prediction of Blood Glucose Level

摘要： 数据预处理是时间序列数据分析的关键部分。 来自联网医疗设备的数据在采集过程中经常存在缺失或异常值。 处理这种情况需要额外的假设和领域知识。 这可能耗时，并可能导致显著偏差，从而影响预测模型的准确性，进而影响医学解释。 为了解决这个问题，我们提出了一种新的深度学习模型，以减轻预处理假设的影响。 该模型架构依赖于变分自编码器（VAE）来生成一个预处理潜在空间，并使用循环VAE来保留数据的时间动态特性。 我们在远程监测数据上展示了这种架构的有效性，以预测糖尿病患者的血糖水平。 我们的结果表明，在准确性方面优于现有的最先进方法和架构。 显示英文摘要

摘要： Data preprocessing is a critical part of time series data analysis. Data from connected medical devices often have missing or abnormal values during acquisition. Handling such situations requires additional assumptions and domain knowledge. This can be time-consuming, and can introduce a significant bias affecting predictive model accuracy and thus, medical interpretation. To overcome this issue, we propose a new deep learning model to mitigate the preprocessing assumptions. The model architecture relies on a variational auto-encoder (VAE) to produce a preprocessing latent space, and a recurrent VAE to preserve the temporal dynamics of the data. We demonstrate the effectiveness of such an architecture on telemonitoring data to forecast glucose-level of diabetic patients. Our results show an improvement in terms of accuracy with respect of existing state-of-the-art methods and architectures.