AI Models Offer New Hope for Personalized GAD Treatment

**Researchers from Penn State are pioneering the use of AI to combat the persistent issue of relapse in individuals with Generalized Anxiety Disorder (GAD).** **Machine learning models** were employed to analyze over 80 baseline factors, including psychological, sociodemographic, and health variables, from a sample of 126 individuals diagnosed with GAD. These individuals were part of the U.S. National Institutes of Health's MIDUS longitudinal study. **The models identified 11 critical factors predictive of recovery or nonrecovery over a nine-year period with up to 72% accuracy.** Key recovery factors include higher education, older age, more social support, and positive affect, while nonrecovery factors include depressed affect and daily discrimination. The study underscores the potential for using AI to develop **evidence-based, personalized treatments** for GAD, potentially addressing comorbid conditions like depression. **Candice Basterfield** and **Michelle Newman** highlighted the enhanced predictive power of machine learning, enabling a deeper understanding of factor interactions, which could lead to more tailored treatment approaches. This research received support from the **U.S. National Institutes of Health.**