用心理生理测试数据预测认知年龄，助力远程健康监测
Using machine learning methods to predict cognitive age from psychophysiological tests
- 基于反应时、记忆等心理测试构建特征，输入机器学习模型
- 通过回归预测个体认知年龄，为老年认知衰退提供量化指标
- 适合关注老龄化健康、移动医疗筛查的科研与临床人员
本研究提出一种新方法，利用心理生理测试预测认知年龄。受试者完成多项心理测试，涵盖反应时间、认知冲突、短期记忆、语言功能、颜色与空间知觉等。根据测试结果，计算每位受试者的平均完成时间、正确率比例、颜色视野测试的平均绝对差值、明斯特矩阵中猜测词数等参数。这些特征经预处理后用于训练机器学习回归模型，以预测个体认知年龄。研究成果有助于推动基于移动设备的远程健康筛查，为认知老化诊断与监测提供支持。
原文摘要 · Abstract (English)
This study introduces a novel method for predicting cognitive age using psychophysiological tests. To determine cognitive age, subjects were asked to complete a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. Based on the tests completed, the average completion time, proportion of correct answers, average absolute delta of the color campimetry test, number of guessed words in the Münsterberg matrix, and other parameters were calculated for each subject. The obtained characteristics of the subjects were preprocessed and used to train a machine learning algorithm implementing a regression task for predicting a person's cognitive age. These findings contribute to the field of remote screening using mobile devices for human health for diagnosing and monitoring cognitive aging.
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