A machine-learning method discovered a hidden clue in people's language predictive of the later emergence of psychosis –-i.e., the frequent use of words associated with sound. A paper published by the Nature-published journal npj Schizophrenia published the findings by scientists at Emory University and Harvard University online on June 13, 2019. The open-access article is titled “A machine learning approach to predicting psychosis using semantic density and latent content analysis.” The researchers also developed a new machine-learning method to more precisely quantify the semantic richness of people's conversational language, a known indicator for psychosis. Their results show that automated analysis of the two language variables -- more frequent use of words associated with sound and speaking with low semantic density, or vagueness -- can predict whether an at-risk person will later develop psychosis with 93 percent accuracy. Even trained clinicians had not noticed how people at risk for psychosis use more words associated with sound than the average, although abnormal auditory perception is a pre-clinical symptom. "Trying to hear these subtleties in conversations with people is like trying to see microscopic germs with your eyes," says Neguine Rezaii, MD, first author of the paper. "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." Dr. Rezaii began work on the paper while she was a resident at Emory School of Medicine's Department of Psychiatry and Behavioral Sciences. She is now at fellow in Harvard Medical School's Department of Neurology.
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