Sleep loss causes decrements in cognitive performance, which increases risks to those in safety-sensitive fields, including medicine and aviation. Mathematical models can be formulated to predict performance decrement in response to sleep loss, with the goal of identifying when an individual may be at highest risk for an accident. This work produces an Ensemble Mixed Effects Model that combines a traditional Linear Mixed Effects (LME) model with a semi-parametric, nonlinear model called Mixed Effects Random Forest (MERF). Using this model, we predict performance on the Psychomotor Vigilance Task (PVT), a test of sustained attention, using biologically motivated features extracted from a dataset containing demographic, sleep, and cognitive test data from 44 healthy participants studied during inpatient sleep loss laboratory experiments.

Our Ensemble Mixed Effects Model accurately predicts an individual’s trend in PVT performance, and fits the data better than prior published models. The ensemble successfully combines MERF’s high rate of peak identification with LME’s conservative predictions. We investigate two questions relevant to this model’s potential use in operational settings: the tradeoff between additional model features versus ease of collecting these features in real-world settings, and how recent a cognitive task must have been administered to produce strong predictions.

This work addresses limitations of previous approaches by developing a predictive model that accounts for interindividual differences and utilizes a nonlinear, semi-parametric method called MERF. We methodologically address the modeling decisions required for this prediction problem, including the choice of cross-validation method. This work is novel in its use of data from a highly-controlled inpatient study protocol that uncouples the influence of the sleep-wake cycle from the endogenous circadian rhythm on the cognitive task being modeled. This uncoupling provides a clearer picture of the model’s real-world predictive ability for situations in which people work at different circadian times (e.g., night- or shift-work).

睡眠不足会导致认知能力下降，这增加了安全敏感领域（包括医学和航空）的风险。可以制定数学模型来预测因睡眠不足而导致的体能下降，目的是确定个人何时可能面临最高的事故风险。这项工作产生了一个集成混合效应模型，它将传统的线性混合效应 (LME) 模型与称为混合效应随机森林 (MERF) 的半参数非线性模型相结合。使用该模型，我们使用从包含人口统计、睡眠和认知测试数据的数据集中提取的生物动机特征预测精神运动警戒任务 (PVT) 的表现，这是一项持续注意力的测试.

我们的集成混合效应模型准确地预测了个人 PVT 表现的趋势，并且比以前发布的模型更好地拟合数据。该集合成功地将 MERF 的高峰值识别率与 LME 的保守预测相结合。我们研究了与该模型在操作环境中的潜在用途相关的两个问题：附加模型特征与在现实世界环境中收集这些特征的难易程度之间的权衡，以及必须如何管理最近的认知任务才能产生强有力的预测。

这项工作通过开发一个预测模型来解决以前方法的局限性，该模型解释了个体间的差异，并利用了一种称为 MERF 的非线性半参数方法。我们在方法上解决了这个预测问题所需的建模决策，包括交叉验证方法的选择。这项工作在使用来自高度控制的住院研究方案的数据方面是新颖的，该方案将睡眠-觉醒周期的影响与内源性昼夜节律对正在建模的认知任务的影响分开。这种解耦提供了模型对人们在不同昼夜工作​​时间（例如，夜间或轮班工作）情况下的真实世界预测能力的更清晰画面。