Personalized glucose forecasting for type 2 diabetes using data assimilation.
Personalized glucose forecasting for type 2 diabetes using data assimilation.
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DOI：
10.1371/journal.pcbi.1005232
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发表时间：
2017-04
影响因子：
4.3
通讯作者：
Mamykina L
中科院分区：
文献类型：
--
作者：
Albers DJ;Levine M;Gluckman B;Ginsberg H;Hripcsak G;Mamykina L
2 型糖尿病导致 8% 的美国人过早死亡并降低生活质量。营养管理对于维持血糖控制至关重要，但由于营养对血糖反应的个体差异很大，因此很难实现。预测不同膳食对血糖的影响不仅对糖尿病患者而且对糖尿病教育专家来说都是一个挑战。可以准确预测特定膳食对个体血糖水平影响的个性化计算模型可以作为新一代糖尿病患者决策支持工具的引擎。然而，为了在实践中发挥作用，这些计算引擎需要根据有限的数据集生成准确的预测，这与 2 型糖尿病患者典型的自我监测实践相一致。本文使用三种预测机器：（i）数据同化，这是一种借用大气物理学和工程学的技术，使用贝叶斯模型将数据与机械模型中表示的人类知识相结合，以生成实时、个性化、适应性强的血糖预测； (ii) 资料同化输出的模型平均； (iii) 动态高斯过程模型回归。所提出的数据同化机是本文的主要焦点，它使用改进的双无迹卡尔曼滤波器来估计状态和参数，从而个性化机械模型。模型选择用于针对个体及其测量特征进行个性化的模型选择。数据同化预测是根据 2 型糖尿病患者捕获的实际餐后血糖测量值以及经验丰富的糖尿病教育者在审查同一个体的一组历史营养记录和血糖测量值后生成的预测进行经验评估的。评估表明，数据同化预测与特定的葡萄糖测量结果进行了很好的比较，并且在准确性方面达到或超过了专家预测。最后，我们研究了将预测呈现为预测衍生范围量的方法，并评估了这些范围的比较优势。 2 型糖尿病是一种毁灭性的疾病，需要患者不断自我管理血糖、胰岛素、营养和运动。然而，葡萄糖和胰岛素动态是复杂的、不稳定的、非线性的和个体依赖性的，使得糖尿病的自我管理成为一项复杂的任务。为了帮助减轻患者的一些困难，我们开发了一种基于营养的个性化、实时血糖预测方法。具体来说，我们基于高斯过程模型和数据同化创建和评估计算机器，利用两种机制模型的生理学知识，为 2 型糖尿病患者实时生成个性化的、基于营养的血糖预测，该预测对于稀疏数据和非平稳患者来说是稳健的。我们的计算引擎被认为可用于糖尿病自我管理。
Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual’s blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges. Type 2 diabetes is a devastating disease that requires constant patient self-management of glucose, insulin, nutrition and exercise. Nevertheless, glucose and insulin dynamics are complicated, nonstationary, nonlinear, and individual-dependent, making self-management of diabetes a complex task. To help alleviate some of the difficulty for patients, we develop a method for personalized, real-time, glucose forecasting based on nutrition. Specifically, we create and evaluate the computational machinery based on both Gaussian process models and data assimilation that leverages the physiologic knowledge of two mechanistic models to produce a personalized, nutrition-based glucose forecast for individuals with type 2 diabetes in real time that is robust to sparse data and nonstationary patients. Our computational engine was conceived to be of potential use for diabetes self-management.