AI language analysis helps clinicians predict psychosis with 93% accuracy

Machine learning was able not only to detect speech patterns indicative of psychosis, but also to identify a new pattern associated with the prodromal phase of psychosis, enabling an algorithm to predict the later emergence of psychosis with more than 90 percent accuracy, according to a study published June 13 in npj Schizophrenia.

Scientists at Emory University in Atlanta developed an algorithm, trained to recognize common patterns of speech using thousands of Reddit posts, that could quantify an individual's semantic density, a known indicator of psychosis. In the process of using the algorithm to analyze the speech patterns of individuals with and without symptoms of psychosis, the scientists discovered another, previously unknown indicator: the frequent use of words associated with voices and sounds.

As a result, when both semantic density and the use of sound-related words were taken into account, the algorithm was able to predict the emergence of psychosis with 93 percent accuracy. In contrast, clinicians are able to predict psychosis in those with prodromal syndrome with about 80 percent accuracy.

"The automated technique we've developed is a really sensitive tool to detect these hidden patterns. It's like a microscope for warning signs of psychosis," Neguine Rezaii, MD, first author of the paper, said. "In the clinical realm, we often lack precision…We need more quantified, objective ways to measure subtle variables, such as those hidden within language usage."

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