用驾驶轨迹目的地预测老年人轻度认知障碍，准确率超68%。
Predicting Mild Cognitive Impairment Using Naturalistic Driving and Trip Destination Modeling
- 通过地理编码分析驾驶者常去的地点（如医院、工作地）来判断认知状态。
- C5.0模型在识别认知障碍时召回率达68%，误判率低。
- 适合做老年驾驶安全监测与早期干预的科研与临床应用。
了解轻度认知障碍（MCI）与驾驶行为的关系对提升老年驾驶员道路安全至关重要。本研究创新性地利用地理编码技术，分析内布拉斯加州老年驾驶员的驾驶习惯，重点关注其前往家庭、工作、医疗就诊、社交活动及办事等特定目的地的行为模式。采用两步法：先通过数据可视化探索，再结合C5.0、随机森林和支持向量机等机器学习模型，评估这些基于位置的变量在预测认知障碍中的有效性。结果表明，C5.0模型表现稳健，中位召回率达到0.68，说明该方法可有效识别68%的认知障碍驾驶员，显著降低漏诊风险。研究证实了生活空间变量在认知衰退预测中的潜力，为早期干预与个性化支持提供了新路径。
原文摘要 · Abstract (English)
Understanding the relationship between mild cognitive impairment (MCI) and driving behavior is essential for enhancing road safety, particularly among older adults. This study introduces a novel approach by incorporating specific trip destinations-such as home, work, medical appointments, social activities, and errands-using geohashing to analyze the driving habits of older drivers in Nebraska. We employed a two-fold methodology that combines data visualization with advanced machine learning models, including C5.0, Random Forest, and Support Vector Machines, to assess the effectiveness of these location-based variables in predicting cognitive impairment. Notably, the C5.0 model showed a robust and stable performance, achieving a median recall of 0.68, which indicates that our methodology accurately identifies cognitive impairment in drivers 68\% of the time. This emphasizes our model's capacity to reduce false negatives, a crucial factor given the profound implications of failing to identify impaired drivers. Our findings underscore the innovative use of life-space variables in understanding and predicting cognitive decline, offering avenues for early intervention and tailored support for affected individuals.
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