Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study.
Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study.
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DOI：
10.3390/geriatrics6020045
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发表时间：
2021-04-23
期刊：
影响因子：
--
通讯作者：
Li G
中科院分区：
文献类型：
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作者：
Di X;Shi R;DiGuiseppi C;Eby DW;Hill LL;Mielenz TJ;Molnar LJ;Strogatz D;Andrews HF;Goldberg TE;Lang BH;Kim M;Li G
新出现的证据表明，驾驶行为的非典型变化可能是轻度认知障碍（MCI）和痴呆的早期信号。本研究旨在评估自然驾驶数据和机器学习技术在预测老年人MCI和痴呆事件中的效用。对2977名老年驾驶员纵向研究参与者长达45个月的车载记录设备每月驾驶数据进行处理，以生成29个测量驾驶行为，空间和性能的变量。事件MCI和痴呆病例（n = 64），确定从病历审查和年度访谈。随机森林被用来分类参与者MCI/痴呆状态在随访期间。随机森林在区分MCI/痴呆状态方面的F1得分仅基于人口统计学特征（年龄，性别，种族/民族和教育）为29%，仅基于驱动变量为66%，基于人口统计学特征和驱动变量为88%。特征重要性分析显示，年龄是MCI和痴呆的最佳预测因素，其次是离家15英里内旅行的百分比、种族/民族、每次旅行链的分钟数（即，在家中开始和结束的行程长度）、每次行程的分钟数以及减速率≥ 0.35 g的紧急制动事件数。如果得到验证，这项研究中开发的算法可以为早期检测和管理老年驾驶员的MCI和痴呆症提供一种新的工具。
Emerging evidence suggests that atypical changes in driving behaviors may be early signals of mild cognitive impairment (MCI) and dementia. This study aims to assess the utility of naturalistic driving data and machine learning techniques in predicting incident MCI and dementia in older adults. Monthly driving data captured by in-vehicle recording devices for up to 45 months from 2977 participants of the Longitudinal Research on Aging Drivers study were processed to generate 29 variables measuring driving behaviors, space and performance. Incident MCI and dementia cases (n = 64) were ascertained from medical record reviews and annual interviews. Random forests were used to classify the participant MCI/dementia status during the follow-up. The F1 score of random forests in discriminating MCI/dementia status was 29% based on demographic characteristics (age, sex, race/ethnicity and education) only, 66% based on driving variables only, and 88% based on demographic characteristics and driving variables. Feature importance analysis revealed that age was most predictive of MCI and dementia, followed by the percentage of trips traveled within 15 miles of home, race/ethnicity, minutes per trip chain (i.e., length of trips starting and ending at home), minutes per trip, and number of hard braking events with deceleration rates ≥ 0.35 g. If validated, the algorithms developed in this study could provide a novel tool for early detection and management of MCI and dementia in older drivers.