Artificial Intelligence To Identify PTSD By Testing Voices – Market News Wire 24
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Artificial Intelligence To Identify PTSD By Testing Voices

A new study found that a specially crafted computer program could aid in diagnosing PTSD (post-traumatic stress disorder) in veterans by testing their voices. The study was published in the journal Depression and Anxiety. The study found that an AI (artificial intelligence) tool can differentiate—with almost 89% accuracy—amongst the voices of those having PTSD or not having PTSD. Charles R. Marmar—Senior Study Author and Chair of the Department of Psychiatry from NYU School of Medicine—said, “Our results suggest that speech-based traits can be utilized to identify this disease and with further modification and validation, might be used in the clinic in the upcoming time.”

Over 70% of adults globally encounter a traumatic incident at some point in their lives, with a minimum of 12% of people in some struggling nations undergoing from PTSD. Those with the medical condition experience persistent and strong distress when reminiscence about a triggering event. The study authors state that PTSD identification is most often decided by a self-report assessment or clinical interview, both naturally prone to biases. This has induced efforts to advance objective, measurable, physical indicators of PTSD progression, much like lab values for medical circumstances, but the improvement has been slow. In the present study, the research group utilized a statistical and machine learning technique—known as random forests—having the capability to “learn” how to categorize individuals on the basis of examples.

On a similar note, recently, a study tracked a link amid PTSD healing utilization and compensation exams. According to a recent study by YU’s (Yale University) Department of Psychiatry researchers, veterans who wanted compensation for job-related PTSD were more possibly to attend PTSD-correlated treatment sessions prior to their compensation exams than later, but only if the experts had strong beliefs on a treatment-compensation connection. The study was published in PLOS One.

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