An Improved Methodology for Individualized Performance Prediction of Sleep-Deprived Individuals with the Two-Process Model
An Improved Methodology for Individualized Performance Prediction of Sleep-Deprived Individuals with the Two-Process Model
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DOI：
10.1093/sleep/32.10.1377
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发表时间：
2009-10-01
期刊：
影响因子：
5.6
通讯作者：
Reifman, Jaques
中科院分区：
文献类型：
--
作者：
Rajaraman, Srinivasan;Gribok, Andrei V.;Reifman, Jaques
我们提出了一种基于睡眠调节的两个过程模型的方法，用于开发个体化的生物数学模型，预测总睡眠丧失的个体的表现障碍。这种新方法在两个重要方面推进了我们以前的工作。首先，它使模型定制能够在个人的第一个绩效测量可用时立即开始。这是通过最佳组合的性能信息，从个人的性能测量与先验性能信息，使用贝叶斯框架，同时保留的战略转化为一系列的线性优化问题的非线性优化问题，找到两个过程的模型参数的最佳估计。其次，通过利用两个过程模型的线性表示，这种新方法使分析计算的模型预测的形式的预测区间的可靠性的统计为基础的措施。两个不同的数据集被用来评估所提出的方法。利用叠加有白色高斯噪声的模拟数据的结果表明，新方法的帕拉估计精度比以前的方法提高了50%到90%。此外，分析计算的预测区间的准确性进行了验证，通过蒙特卡罗模拟。参与实验室研究（82小时的总睡眠损失）的三种睡眠损失表型的受试者的结果表明，所提出的方法产生的个性化预测比组平均预测模型准确率高出43%，平均比基于我们以前的方法的个性化预测准确率高出10%。
We present a method based on the two-process model of sleep regulation for developing individualized biomathematical models that predict performance impairment for individuals subjected to total sleep loss. This new method advances our previous work in two important ways. First, it enables model customization to start as soon as the first performance measurement from an individual becomes available. This was achieved by optimally combining the performance information obtained from the individual's performance measurements with a priori performance information using a Bayesian framework, while retaining the strategy of transforming the nonlinear optimization problem of finding the optimal estimates of the two-process model parameters into a series of linear optimization problems. Second, by taking advantage of the linear representation of the two-process model, this new method enables the analytical computation of statistically based measures of reliability for the model predictions in the form of prediction intervals. Two distinct data sets were used to evaluate the proposed method. Results using simulated data with superimposed white Gaussian noise showed that the new method yielded 50% to 90% improvement in para rameter-estimate accuracy over the previous method. Moreover, the accuracy of the analytically computed prediction intervals was validated through Monte Carlo simulations. Results for subjects representing three sleep-loss phenotypes who participated in a laboratory study (82 h of total sleep loss) indicated that the proposed method yielded individualized predictions that were up to 43% more accurate than group-average prediction models and, on average, 10% more accurate than individualized predictions based on our previous method.