Cognition in midlife is an important predictor of age-related mental decline and statistical models that predict cognitive performance can be useful for predicting decline. However, existing models struggle to capture complex relationships between physical, sociodemographic, psychological and mental health factors that effect cognition. Using data from an observational, cohort study, Midlife in the United States (MIDUS), we modeled a large number of variables to predict executive function and episodic memory measures. We used cross-sectional and longitudinal outcomes with varying sparsity, or amount of missing data. Deep neural network (DNN) models consistently ranked highest in all of the cognitive performance prediction tasks, as assessed with root mean squared error (RMSE) on out-of-sample data. RMSE differences between DNN and other model types were statistically significant (T(8) = -3.70; p < 0.05). The interaction effect between model type and sparsity was significant (F(9)=59.20; p < 0.01), indicating the success of DNNs can partly be attributed to their robustness and ability to model hierarchical relationships between health-related factors. Our findings underscore the potential of neural networks to model clinical datasets and allow better understanding of factors that lead to cognitive decline.
翻译:中生期认知是预测认知性下降的与年龄有关的精神衰落和统计模型的重要预测因素,预测认知性表现的统计模型可用于预测衰退。然而,现有模型努力捕捉影响认知性衰退的物理、社会人口、心理和心理健康因素之间的复杂关系。利用观察、群群研究、美国中生期研究(MIDUS)的数据,我们模拟了大量变量,以预测执行功能和偶发记忆量。我们使用跨部门和纵向结果,其范围不同,或缺少的数据数量也不同。深神经网络(DNN)模型在所有认知性预测任务中一贯排名最高,其评估是在抽样数据中根正方形错误(RMSE),RMSE DNN和其他类型模型之间的差异具有统计意义(T(8)=-3.70;p < 0.05),模型类型和神经紧张性之间的相互作用作用很大(F(9)=59.20;p < 0.01),表明DNNW的成功部分可归因于其稳健的模型性以及能力,因为其模型的模型性能更好理解与临床认知性因素相关的数据关系。我们强调与下降因素。