Fit2Drive:对年龄较大的驾驶员进行认知问题查
Ruth Tappen1, David Newman1, Monica Rosselli2
1Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, USA.
概括
一个新的算法,Fit2Drive,使用认知测试准确预测老司机的驾驶能力. 该工具可以帮助识别需要进一步评估道路安全的司机.
科学领域:
- 老年学是一门学科.
- 神经科学是一个神经科学.
- 心理测量 心理测量 心理测量
背景情况:
- 年龄较大的司机在道路上面临更多的风险.
- 认知能力下降可能会影响驾驶能力.
- 预测性查工具是需要在办公室使用的.
研究的目的:
- 确定用于预测驾驶能力的认知测试最有效的组合.
- 为办公室内部管理制定一个节的选措施.
- 评估一种新型预测算法的诊断准确性.
主要方法:
- 一项涉及412名老年司机的心理测量研究.
- 认知测试 (MMSE,Trail Making Tests A & B,时钟测试,霍普金斯口头学习测试,驾驶健康清单) 与道路驾驶评估的比较.
- 使用后勤回归和接收器操作特征曲线开发一个预测算法 (Fit2Drive).
主要成果:
- 步道制作测试B的时间与道路性能高度相关 (r = -0.713).
- 结合Trail Making Test B和Mini-Mental State Exam (MMSE) 的分数,Fit2Drive算法实现了很高的预测准确性 (曲线下的面积=0.915).
- 该算法在通过路面评估时显示出91.5%的预测准确度.
结论:
- "Fit2Drive"算法显示,对于精确选老年司机的驾驶健康状况,有很大的潜力.
- 这种工具可以促进在临床环境中早期识别有风险的驾驶员.
- 需要进一步进行严格的测试,以确认其在例行患者咨询中的有用性.
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