A person with generalized anxiety disorder (GAD), a condition that is characterized by daily excessive anxiety lasting for at least six months, is a high relay rate even after receiving treatment.
According to Artificial Intelligence (AI) model researchers, long -term recovery and better privatization can help doctors identify factors to predict patient treatment.
Researchers used a form of AI called Machine Learning to analyze more than 80 baseline factors – from psychological and sociological to health and lifestyle variables – diagnosed with GAD for 126 unnamed individuals. Data came from america
Longitudinal studies of the National Institute of Health are called Midlife in the United States, sampling the health data of continental American inhabitants between the ages of 25 to 74 years, which was first interviewed in 1995-96.
The machine learning model identified the 11 variables, which appear to be the most important for predicting recovery and non-profit, with 72% accuracy at the end of the nine-year period. Researchers published their conclusions in the march issue of The Journal of Annuxity Disorder.
The lead study writer and doctoral candidate Candice Bastarfield in Pen State said, “East research has shown a lot of relay papers in GAD, and there is also limited accuracy in the doctor’s decision to predict prolonged results.”
“This research suggests that the machine learning model shows good accuracy, sensitivity and uniqueness in predicting who will not get rid of and will not be cured. These prophets of recovery can be really important to help in making evidence-based, personal treatment for long-term recovery.”
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Researchers found that higher education levels, old age, more friendly support, high waist-to-hip-numer and high positive effects, or more cheerful, were the most important for recovery in that order.
Meanwhile, depressed effects, daily discrimination, greater number of sessions with a mental health professional in the last 12 months and more number of visits for medical doctors in the last 12 months proved to be the most important for predicting non -profit.
Researchers validated model findings by comparing machine learning predictions to mids data, found that the estimated recovery variables were tracked with 95 participants who showed no GAD symptoms at the end of a nine -year period.
Conclusions suggest that physicians can use AI to identify these variables and to personalize treatment for GAD patients – especially for those who make compound diagnosis, according to researchers.
Also read: Can the secret of reducing anxiety be hidden in your intestine? Here’s how to fix your mood
Note the readers: This article is only for informative purposes and is not an option for professional medical advice. Always consult your doctor with any question about a medical condition.
