Bayesian Optimization of Personalized Models for Patient Vital-Sign Monitoring
Bayesian Optimization of Personalized Models for Patient Vital-Sign Monitoring
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DOI：
10.1109/jbhi.2017.2751509
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发表时间：
2018-03-01
影响因子：
7.7
通讯作者：
Clifton, David A.
中科院分区：
文献类型：
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作者：
Colopy, Glen Wright;Roberts, Stephen J.;Clifton, David A.
高斯过程回归（GPR）提供了一种生成患者生命体征时间序列的灵活个性化模型的方法。这些模型可以以基于人群的模型所不能的方式进行有用的临床推断。使用个性化模型的一个挑战是，它们必须能够适应广泛的参数化，以适应人群中任何患者的合理生理学。此外，当模型根据个体患者的生理知识进行正则化时，通常会实现最佳性能。在本文中，我们描述了一种方法来建立GP模型具有不同的复杂性（通过协方差内核）和正则化（通过固定先验的超参数）在患者特定的水平上，强大的生命体征预测的目的。为此，我们的研究结果提供了支持两个主要假设的证据：1）对于有用的临床任务，例如生命体征预测，使用患者特定模型可以优于基于人群的模型; 2）由于搜索空间维度之间的高度相关性，这些模型的（超）参数的最佳值最好通过复杂的优化方法确定。由此产生的模型足够强大，可以告知临床医生患者的生命体征轨迹，并警告即将恶化。
Gaussian process regression (GPR) provides a means to generate flexible personalized models of time series of patient vital signs. These models can perform useful clinical inference in ways that population-based models cannot. A challenge for the use of personalized models is that they must be amenable to a wide range of parameterizations, to accommodate the plausible physiology of any patient in the population. Additionally, optimal performance is typically achieved when models are regularized in light of the knowledge of the physiology of the individual patient. In this paper, we describe a method to build GP models with varying complexity (via covariance kernels) and regularization (via fixed priors over hyperparameters) on a patient-specific level, for the purpose of robust vital-sign forecasting. To this end, our results present evidence in support of two main hypotheses: 1) the use of patient-specific models can outperform population-based models for useful clinical tasks, such as vital-sign forecasting; and 2) the optimal values of (hyper) parameters of these models are best determined by sophisticated methods of optimization, due to high correlation between dimensions of the search space. The resulting models are sufficiently robust to inform clinicians of a patient's vital-sign trajectory and warn of imminent deterioration.