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, MCMR-ZB-T, 363 Miller Dr., Fort Detrick, MD 21702, USA.
J Appl Physiol (1985). 2008 Feb;104(2):459-68. doi: 10.1152/japplphysiol.00877.2007. Epub 2007 Dec 13.
We present a new method for developing individualized biomathematical models that predict performance impairment for individuals restricted to total sleep loss. The underlying formulation is based on the two-process model of sleep regulation, which has been extensively used to develop group-average models. However, in the proposed method, the parameters of the two-process model are systematically adjusted to account for an individual's uncertain initial state and unknown trait characteristics, resulting in individual-specific performance prediction models. The method establishes the initial estimates of the model parameters using a set of past performance observations, after which the parameters are adjusted as each new observation becomes available. Moreover, by transforming the nonlinear optimization problem of finding the best estimates of the two-process model parameters into a set of linear optimization problems, the proposed method yields unique parameter estimates. Two distinct data sets are used to evaluate the proposed method. Results of simulated data (with superimposed noise) show that the model parameters asymptotically converge to their true values and the model prediction accuracy improves as the number of performance observations increases and the amount of noise in the data decreases. Results of a laboratory study (82 h of total sleep loss), for three sleep-loss phenotypes, suggest that individualized models are consistently more accurate than group-average models, yielding as much as a threefold reduction in prediction errors. In addition, we show that the two-process model of sleep regulation is capable of representing performance data only when the proposed individualized model is used.
我们提出了一种新方法，用于开发个性化生物数学模型，该模型可预测因完全睡眠剥夺而受限的个体的性能损害。其基本公式基于睡眠调节的双过程模型，该模型已被广泛用于开发群体平均模型。然而，在所提出的方法中，双过程模型的参数会被系统地调整，以考虑个体不确定的初始状态和未知的特质特征，从而得到个体特定的性能预测模型。该方法使用一组过去的性能观测值来建立模型参数的初始估计值，之后随着每个新观测值的获得对参数进行调整。此外，通过将寻找双过程模型参数最佳估计值的非线性优化问题转化为一组线性优化问题，所提出的方法产生了唯一的参数估计值。使用两个不同的数据集来评估所提出的方法。模拟数据（带有叠加噪声）的结果表明，随着性能观测次数的增加和数据中噪声量的减少，模型参数渐近收敛到其真实值，并且模型预测准确性提高。一项实验室研究（82小时完全睡眠剥夺）针对三种睡眠剥夺表型的结果表明，个性化模型始终比群体平均模型更准确，预测误差最多可降低三倍。此外，我们表明，只有在使用所提出的个性化模型时，睡眠调节的双过程模型才能表示性能数据。
