Optimization of biomathematical model predictions for cognitive performance impairment in individuals: Accounting for unknown traits and uncertain states in homeostatic and circadian processes
Optimization of biomathematical model predictions for cognitive performance impairment in individuals: Accounting for unknown traits and uncertain states in homeostatic and circadian processes
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DOI：
10.1093/sleep/30.9.1129
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发表时间：
2007-09-01
期刊：
影响因子：
5.6
通讯作者：
Dinges, David F.
中科院分区：
文献类型：
--
作者：
Van Dongen, Hans P. A.;Mott, Christopher G.;Dinges, David F.
当前疲劳和性能的生物数学模型不能准确地预测具有先验未知程度的对睡眠丧失的特质脆弱性的个体的认知性能，当初始条件不确定时不能可靠地预测性能，并且不能产生预测精度的统计有效估计，这些限制降低了它们用于预测操作环境中个体的性能的有用性。为了克服这3个局限性，开发了一种新的建模方法，基于扩展的统计技术称为贝叶斯预测。在睡眠调节的两个过程模型中实施了扩展的贝叶斯预测程序，该模型已被用于预测性能的基础上的组合的睡眠稳态过程和昼夜节律过程。采用双过程模型和贝叶斯预测程序来预测个体受试者在未知特征和不确定状态下的表现，需要对3个特征参数（稳态建立率、昼夜节律幅度和基础表现水平）和2个初始状态参数（初始稳态和昼夜节律相位角）进行针对受试者的优化。在大的人口中的性状参数的分布的先验信息提取的心理警觉性测试（PVT）的性能测量在10名参加了88小时的总睡眠剥夺的实验室实验。本实验中另外3名受试者的PVT性能数据事先预留用于前瞻性计算机模拟。模拟涉及每次下一次性能测量可用时更新特定于受试者的模型参数，然后提前24小时预测性能。将预测结果与受试者的实际数据进行比较发现，随着手头上的个人数据越来越多，性能预测变得越来越准确，95%置信区间逐渐变小，因为模型参数有效地收敛到最能表征每个人的参数。即使进行更具挑战性的模拟（模拟初始稳态的变化;模拟数据稀疏），预测仍然比单独使用双过程模型要准确得多。虽然这里所描述的工作仍然局限于具有稳定昼夜节律的巩固清醒期，但迄今为止获得的结果表明，贝叶斯预测程序可以成功地克服在操作环境中认知性能的生物数学预测的一些主要突出挑战。
Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy, These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became available, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became increasingly more accurate and had progressively smaller 95% confidence intervals, as the model parameters converged efficiently to those that best characterized each individual. Even when more challenging simulations were run (mimicking a change in the initial homeostatic state; simulating the data to be sparse), the predictions were still considerably more accurate than would have been achieved by the two-process model alone. Although the work described here is still limited to periods of consolidated wakefulness with stable circadian rhythms, the results obtained thus far indicate that the Bayesian forecasting procedure can successfully overcome some of the major outstanding challenges for biomathematical prediction of cognitive performance in operational settings.