使用上下文信息改进血糖预测
Using Contextual Information to Improve Blood Glucose Prediction
摘要：
血糖值预测是糖尿病管理中的重要任务。虽然据报道，葡萄糖浓度对情绪，身体活动，压力，饮食等社会背景敏感，以及糖尿病病理学的影响，但我们需要对数据和方法进行更多的研究，以将和评估有关这种时间背景的信号纳入预测模型。个人生成的数据源，例如主动贡献的调查以及来自社交媒体的被动挖掘的数据，提供了捕获这种背景的机会，然而，这种数据的自我报告性质和稀疏性意味着这种数据比生理测量（例如血糖值本身）更嘈杂且更不具体。因此，在这里，我们提出了一个高斯过程模型来解决这些数据的挑战，并结合血糖和潜在特征表示的上下文数据的一个新的多信号血糖预测任务。我们发现这种方法优于多变量数据的常用方法，以及单独使用血糖值。通过对两个具有不同形式背景信息的血糖数据集进行稳健评估，我们得出结论，多信号高斯过程可以通过使用背景信息来改善血糖预测，并可能为血糖预测研究和实践提供重大转变。
英文摘要：
Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the influence of diabetes pathologies, we need more research on data and methodologies to incorporate and evaluate signals about such temporal context into prediction models. Person-generated data sources, such as actively contributed surveys as well as passively mined data from social media offer opportunity to capture such context, however the self-reported nature and sparsity of such data mean that such data are noisier and less specific than physiological measures such as blood glucose values themselves. Therefore, here we propose a Gaussian Process model to both address these data challenges and combine blood glucose and latent feature representations of contextual data for a novel multi-signal blood glucose prediction task. We find this approach outperforms common methods for multi-variate data, as well as using the blood glucose values in isolation. Given a robust evaluation across two blood glucose datasets with different forms of contextual information, we conclude that multi-signal Gaussian Processes can improve blood glucose prediction by using contextual information and may provide a significant shift in blood glucose prediction research and practice.
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