A machine-learning method discovered a hidden clue in people’s language predictive of the later emergence of psychosis — the frequent use of words associated with sound.
copyright by www.technologynetworks.com
The journal npj Schizophrenia published the findings by scientists at Emory University and Harvard University.
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, 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.”
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 a fellow in Harvard Medical School’s Department of Neurology.
“It was previously known that subtle features of future psychosis are present in people’s language, but we’ve used machine learning to actually uncover hidden details about those features,” says senior author Phillip Wolff, a professor of psychology at Emory. Wolff’s lab focuses on language semantics and machine learning to predict decision-making and mental health.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
“Our finding is novel and adds to the evidence showing the potential for using machine learning to identify linguistic abnormalities associated with mental illness,” says co-author Elaine Walker, an Emory professor of psychology and neuroscience who researches how schizophrenia and other psychotic disorders develop.
The onset of schizophrenia and other psychotic disorders typically occurs in the early 20s, with warning signs — known as prodromal syndrome — beginning around age 17. About 25 to 30 percent of youth who meet criteria for a prodromal syndrome will develop schizophrenia or another psychotic disorder.
Using structured interviews and cognitive tests, trained clinicians can predict psychosis with about 80 percent accuracy in those with a prodromal syndrome. Machine-learning research is among the many ongoing efforts to streamline diagnostic methods, identify new variables, and improve the accuracy of predictions.
Currently, there is no cure for psychosis.
“If we can identify individuals who are at risk earlier and use preventive interventions, we might be able to reverse the deficits,” Walker says. “There are good data showing that treatments like cognitive-behavioral therapy can delay onset, and perhaps even reduce the occurrence of psychosis.”[…]
Credit: Photo by Etienne Boulanger on Unsplash https://unsplash.com/@etienneblg
read more – copyright by www.technologynetworks.com
A machine-learning method discovered a hidden clue in people’s language predictive of the later emergence of psychosis — the frequent use of words associated with sound.
copyright by www.technologynetworks.com
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, 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.”
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 a fellow in Harvard Medical School’s Department of Neurology.
“It was previously known that subtle features of future psychosis are present in people’s language, but we’ve used machine learning to actually uncover hidden details about those features,” says senior author Phillip Wolff, a professor of psychology at Emory. Wolff’s lab focuses on language semantics and machine learning to predict decision-making and mental health.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
“Our finding is novel and adds to the evidence showing the potential for using machine learning to identify linguistic abnormalities associated with mental illness,” says co-author Elaine Walker, an Emory professor of psychology and neuroscience who researches how schizophrenia and other psychotic disorders develop.
The onset of schizophrenia and other psychotic disorders typically occurs in the early 20s, with warning signs — known as prodromal syndrome — beginning around age 17. About 25 to 30 percent of youth who meet criteria for a prodromal syndrome will develop schizophrenia or another psychotic disorder.
Using structured interviews and cognitive tests, trained clinicians can predict psychosis with about 80 percent accuracy in those with a prodromal syndrome. Machine-learning research is among the many ongoing efforts to streamline diagnostic methods, identify new variables, and improve the accuracy of predictions.
Currently, there is no cure for psychosis.
“If we can identify individuals who are at risk earlier and use preventive interventions, we might be able to reverse the deficits,” Walker says. “There are good data showing that treatments like cognitive-behavioral therapy can delay onset, and perhaps even reduce the occurrence of psychosis.”[…]
Credit: Photo by Etienne Boulanger on Unsplash https://unsplash.com/@etienneblg
read more – copyright by www.technologynetworks.com
Share this: