Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA; Department of Biomedical Engineering, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA; Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA; Department of Biomedical Informatics, Columbia University, New York, 10032, NY, USA.
Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA.
J Biomed Inform. 2023 Sep;145:104477. doi: 10.1016/j.jbi.2023.104477. Epub 2023 Aug 20.
Prediction of physiological mechanics are important in medical practice because interventions are guided by predicted impacts of interventions. But prediction is difficult in medicine because medicine is complex and difficult to understand from data alone, and the data are sparse relative to the complexity of the generating processes. Computational methods can increase prediction accuracy, but prediction with clinical data is difficult because the data are sparse, noisy and nonstationary. This paper focuses on predicting physiological processes given sparse, non-stationary, electronic health record data in the intensive care unit using data assimilation (DA), a broad collection of methods that pair mechanistic models with inference methods.
A methodological pipeline embedding a glucose-insulin model into a new DA framework, the constrained ensemble Kalman filter (CEnKF) to forecast blood glucose was developed. The data include tube-fed patients whose nutrition, blood glucose, administered insulins and medications were extracted by hand due to their complexity and to ensure accuracy. The model was estimated using an individual's data as if they arrived in real-time, and the estimated model was run forward producing a forecast. Both constrained and unconstrained ensemble Kalman filters were estimated to compare the impact of constraints. Constraint boundaries, model parameter sets estimated, and data used to estimate the models were varied to investigate their influence on forecasting accuracy. Forecasting accuracy was evaluated according to mean squared error between the model-forecasted glucose and the measurements and by comparing distributions of measured glucose and forecast ensemble means.
The novel CEnKF produced substantial gains in robustness and accuracy while minimizing the data requirements compared to the unconstrained ensemble Kalman filters. Administered insulin and tube-nutrition were important for accurate forecasting, but including glucose in IV medication delivery did not increase forecast accuracy. Model flexibility, controlled by constraint boundaries and estimated parameters, did influence forecasting accuracy.
Accurate and robust physiological forecasting with sparse clinical data is possible with DA. Introducing constrained inference, particularly on unmeasured states and parameters, reduced forecast error and data requirements. The results are not particularly sensitive to model flexibility such as constraint boundaries, but over or under constraining increased forecasting errors.
在医学实践中，生理力学的预测非常重要，因为干预措施是根据干预措施的预测影响来指导的。但是，医学中的预测非常困难，因为医学本身非常复杂，仅从数据层面很难理解，而且数据相对于生成过程的复杂性来说非常稀疏。计算方法可以提高预测的准确性，但由于数据稀疏、存在噪声且不稳定，使用临床数据进行预测非常困难。本文专注于使用数据同化（DA），即一种将机械模型与推理方法结合的广泛方法，来预测重症监护病房中稀疏、非平稳的电子健康记录数据中的生理过程。
开发了一种将葡萄糖-胰岛素模型嵌入新的 DA 框架（约束集合卡尔曼滤波器，CEnKF）中的方法学管道，以预测血糖。这些数据包括通过手动提取的接受管饲的患者的数据，因为他们的营养、血糖、输注的胰岛素和药物非常复杂，并且为了确保准确性，需要手动提取。该模型是使用个体数据进行估计的，就好像他们实时到达一样，然后运行估计模型以生成预测。为了比较约束的影响，同时估计了约束和非约束集合卡尔曼滤波器。还改变了约束边界、估计的模型参数集以及用于估计模型的数据，以研究它们对预测准确性的影响。根据模型预测的血糖与测量值之间的均方误差以及比较测量血糖和预测集合均值的分布来评估预测准确性。
与非约束集合卡尔曼滤波器相比，新型 CEnKF 在最小化数据需求的同时，在稳健性和准确性方面有显著提高。准确的预测需要输注的胰岛素和管饲营养，但将葡萄糖纳入静脉内药物输送并不能提高预测准确性。模型灵活性（由约束边界和估计的参数控制）确实会影响预测准确性。
使用 DA 可以实现稀疏临床数据的准确、稳健的生理预测。引入约束推理，特别是对未测量状态和参数的约束推理，可以减少预测误差和数据需求。结果对模型灵活性（如约束边界）不是特别敏感，但过度或不足的约束会增加预测误差。
