AI Models Revolutionize Long-term GAD Recovery Predictions

Research highlights how AI models, particularly machine learning, can accurately predict long-term recovery from generalized anxiety disorder (GAD), allowing for personalized treatment. By analyzing 80 different factors, the models identified key predictors of recovery, offering hope for better management of GAD, which typically shows high relapse rates.

A groundbreaking study reveals that artificial intelligence (AI) models, specifically machine learning, may transform treatment approaches for generalized anxiety disorder (GAD). Characterized by chronic anxiety, GAD often results in high relapse rates even after therapy. Researchers assert that AI could refine predictions about patient recovery, leading to more personalized healthcare strategies.

Utilizing data from 126 anonymized individuals diagnosed with GAD, the study sourced its information from the U.S. National Institutes of Health's Midlife in the United States study. This longitudinal study offered comprehensive data from U.S. residents initially interviewed in 1995-96. Machine learning models assessed over 80 baseline factors, including psychological, sociodemographic, health, and lifestyle variables, identifying 11 primary predictors of recovery with up to 72% accuracy after nine years.

The study, authored by Penn State's Candice Basterfield, revealed that factors such as higher education, older age, and positive affect significantly contributed to recovery. Conversely, higher mental health consultations and experiences of daily discrimination indicated a risk of non-recovery. These findings, published in the Journal of Anxiety Disorders, propose AI as an instrumental tool for clinicians to tailor treatments, especially for GAD patients with complex health profiles. (ANI)

(With inputs from agencies.)