Rajaraman Srinivasan, Gribok Andrei V, Wesensten Nancy J, Balkin Thomas J, Reifman Jaques
Bioinformatics Cell, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.
Sleep. 2009 Oct;32(10):1377-92. doi: 10.1093/sleep/32.10.1377.
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 parameter-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.
我们提出了一种基于睡眠调节双过程模型的方法，用于开发个性化生物数学模型，以预测完全睡眠剥夺个体的性能损害。这种新方法在两个重要方面推进了我们之前的工作。首先，它使得一旦获得个体的首次性能测量结果，模型定制就可以开始。这是通过使用贝叶斯框架将从个体性能测量中获得的性能信息与先验性能信息进行最佳组合来实现的，同时保留了将寻找双过程模型参数最优估计的非线性优化问题转化为一系列线性优化问题的策略。其次，利用双过程模型的线性表示，这种新方法能够以预测区间的形式对模型预测进行基于统计的可靠性分析计算。使用两个不同的数据集来评估所提出的方法。使用叠加白高斯噪声的模拟数据的结果表明，新方法在参数估计精度上比先前方法提高了50%至90%。此外，通过蒙特卡罗模拟验证了分析计算的预测区间的准确性。对参与实验室研究（82小时完全睡眠剥夺）的代表三种睡眠剥夺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高达43%，并且平均而言，比基于我们先前方法的个性化预测准确10%。
