Chen Chen, Brown David C, Al-Hammadi Noor, Bayat Sayeh, Dickerson Anne, Vrkljan Brenda, Blake Matthew, Zhu Yiqi, Trani Jean-Francois, Lenze Eric J, Carr David B, Babulal Ganesh M
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
Department of Biomedical Engineering, Schuluch School of Engineering, University of Calgary, Calgary, AB, Canada.
NPJ Digit Med. 2025 Feb 15;8(1):102. doi: 10.1038/s41746-025-01500-w.
Depression in older adults is often underdiagnosed and has been linked to adverse outcomes, including motor vehicle crashes. With a growing population of older drivers in the United States, innovations in screening methods are needed to identify older adults at greatest risk of decline. This study used machine learning techniques to analyze real-world naturalistic driving data to identify depression status in older adults and examined whether specific demographics and medications improved model performance. We analyzed two years of GPS data from 157 older adults, including 81 with major depressive disorder, using XGBoost and logistic regression models. The top-performing model achieved an area under the curve of 0.86 with driving features combined with total medication use. These findings suggest that naturalistic driving data holds high potential as a functional digital neurobehavioral marker for AI identifying depression in older adults on a national scale, thereby ensuring equitable access to treatment.
老年人的抑郁症往往未得到充分诊断，并且与包括机动车碰撞在内的不良后果有关。随着美国老年驾驶员人口的不断增加，需要创新筛查方法来识别风险最高的老年人。本研究使用机器学习技术分析现实世界中的自然驾驶数据，以识别老年人的抑郁状态，并研究特定的人口统计学特征和药物是否能改善模型性能。我们使用XGBoost和逻辑回归模型，分析了157名老年人（包括81名患有重度抑郁症的老年人）的两年GPS数据。表现最佳的模型在结合驾驶特征和药物使用总量时，曲线下面积达到了0.86。这些发现表明，自然驾驶数据作为一种功能性数字神经行为标志物，在全国范围内利用人工智能识别老年人抑郁症方面具有很高的潜力，从而确保公平获得治疗。
