Individuals with generalized anxiety disorder (GAD), a condition characterized by daily excessive worry lasting at least six months, have a high relapse rate even after receiving treatment. Artificial intelligence (AI) models may help clinicians identify factors to predict long-term recovery and better personalize patient treatment, according to researchers from Penn State.
The researchers used a form of AI called machine learning to analyze more than 80 baseline factors - ranging from psychological and sociodemographic to health and lifestyle variables - for 126 anonymized individuals diagnosed with GAD. The data came from the U.S. National Institutes of Health's longitudinal study called Midlife in the United States, which samples health data from continental U.S. residents aged 25 to 74 who were first interviewed in 1995-96. The machine learning models identified 11 variables that appear most important for predicting recovery and nonrecovery, with up to 72% accuracy, at the end of a nine-year period. The researchers published their findings in the March issue of the Journal of Anxiety Disorders.
"Prior research has shown a very high relapse rate in GAD, and there's also limited accuracy in clinician judgment in predicting long-term outcomes," said Candice Basterfield, lead study author and doctoral candidate at Penn State. "This research suggests that machine learning models show good accuracy, sensitivity and specificity in predicting who will and won't recover from GAD. These predictors of recovery could be really important for helping to create evidence-based, personalized treatments for long-term recovery."
The researchers ran the baseline variables through two machine learning models: a linear regression model that examines the relationship between two variables and plots data points along a nearly straight line, and a nonlinear model that branches out like a tree, splitting and adding new trees and plotting how it self-corrects prior errors. The models identified the 11 variables key to predicting recovery or nonrecovery over the nine-year period, with the linear model outperforming the nonlinear model. The models also identified how important each variable was compared to the others for predicting recovery outcomes.
