AI can help clinicians personalise treatment for Generalized Anxiety Disorder: Study

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.

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.

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 found that higher education level, older age, more friend support, higher waist-to-hip-ratio and higher positive affect, or feeling more cheerful, were most important to recovery, in that order.

Meanwhile, depressed affect, daily discrimination, greater number of sessions with a mental health professional in the past 12 months and greater number of visits to medical doctors in the past 12 months proved most important to predicting nonrecovery.

The researchers validated the model findings by comparing the machine learning predictions to the MIDUS data, finding that the predicted recovery variables tracked with the 95 participants who showed no GAD symptoms at the end of the nine-year period.

The findings suggest that clinicians can use AI to identify these variables and personalize treatment for GAD patients -- especially those with compounding diagnoses, according to the researchers. (ANI)

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