Washington DC Washington DC: A person suffering from generalized anxiety disorder (GAD), who is a specialty of excessive anxiety at least six months, has a high recurrence rate even after receiving treatment. According to the researchers, artificial intelligence (AI) models can help physicians identify factors to predict long -term recovery and to better personalize the patient's treatment.

Researchers have called Machine Learning to analyze more than 80 basic factors for 126 unnamed individuals suffering from GAD – from psychological and socio -artistic to health and lifestyle. Used a form of. Data U.S. Has come from A longitudinal study of the National Institute of Health, called Midlife in the United States, which is the continental U.S. of the age of 25 to 74 years. Samples of health data from residents, who were first interviewed in 1995-96. The machine learning model identified 11 variables that seem to be the most important for predicting recovery and non -intercourse with 72% accuracy at the end of a nine -year period. Researchers published their conclusions in the march issue of the Journal of Angel of Angeli Disorder.

“Previous researches have shown a lot of relax rates in the GAD, and also limited a limited accuracy in a physician's decision to predict long -term results,” said Candice Busterfield.

“This research shows good accuracy, sensitivity and uniqueness to estimate who will be fine with the machine learning model GAD and who is not. These programs of recovery can be really important to help to help in creating evidence-based, personal remedies for long-term recovery.”

Researchers found that higher education level, older age, more friendly support, high waist-to-cooles ratio and high positive effects, or feeling more happy, were the most important for recovery in that order.

Meanwhile, depressive effects, daily discrimination, more sessions with mental health professionals in the last 12 months and more important to predict non-intercourse with medical doctors in the last 12 months have proved to be the most important.

Researchers validated model findings by comparing machine learning forecasts to Midus data, which found that the predicted recoveries were tracked with 95 participants who showed no GAD symptoms at the end of a nine -year period. According to the researchers, findings suggest that physicians can use AI to identify these variables and to personalize treatment for GAD patients – especially for those whose diagnosis is complex.