AI Models Revolutionize Generalized Anxiety Disorder Treatment Predictions

Researchers have utilized machine learning to identify key predictors of recovery in individuals with generalized anxiety disorder (GAD), offering a novel approach to personalizing treatment. Analyzing data from a long-term U.S. study, they achieved 72% accuracy in predicting recovery, highlighting the potential of AI in mental health care.

Individuals suffering from generalized anxiety disorder (GAD) face persistent excessive worry, and despite undergoing treatment, they often experience relapse. Recent research suggests that artificial intelligence (AI) may offer a solution. Researchers are leveraging AI models to predict long-term recovery and personalize treatment to combat this pervasive issue.

The study utilized machine learning to analyze over 80 baseline factors—including psychological, sociodemographic, and lifestyle variables—for 126 anonymized GAD patients. The data, sourced from the National Institutes of Health's Midlife in the United States study, tracked health data of U.S. residents aged 25 to 74, starting in the mid-1990s.

The AI models uncovered 11 key variables influencing recovery and nonrecovery, achieving 72% accuracy after nine years. Published in the Journal of Anxiety Disorders, lead author Candice Basterfield highlighted the potential for these findings to enhance treatment personalization. Key recovery predictors included education level, age, social support, and positive affect, while factors like depression and frequent medical consultations indicated nonrecovery.

(With inputs from agencies.)