Identifying Major Depressive Disorder in Older Adults Through Naturalistic Driving Behaviors and Machine Learning

Naturalistic driving data offers a promising avenue for detecting depression in older adults, with high specificity and recall. The integration of driving behaviors with medication data further enhances predictive accuracy, providing a functional and scalable approach for early identification. This study underscores the potential of AI-driven tools to address diagnostic challenges in geriatric mental health, enabling timely interventions and improving public safety. Future research should explore integrating additional contextual data, such as cognitive assessments or real-time physiological metrics, to refine model accuracy and expand clinical applications.