People’s language may contain clues about the future risk of developing psychosis. Scientists have decided this after studying the subtle characteristics of people’s daily speech.
Researchers at Emory University (Atlanta, Georgia) and Harvard University (Boston, MA) used a machine-learning technique to analyze the language of a group of young people at risk. They identified that they could foretell which individuals would develop psychosis with a precision of 93%.
A recent study on npj schizophrenia describes how the team developed and tested the method.
Lead researcher Phillip Wolff, a professor of psychology at Emory University, states that previous research has already shown that “the subtle features of future psychosis are present in people’s language.” He noted, however, “we use machine learning to discover hidden details about these functions.”
He and his colleagues designed their machine-learning approach to compute two linguistic variables; semantic density and words utilization related to sound.
They concluded that transfer to psychosis is pointed by low semantic density and voices and sounds speech. Low semantic density is a measure of what the team describes as inaccuracy and content poverty.
“This work is a validation study designed to show that indicators of future mental health can be derived from the natural language of people using computational methods,” according to the author.
Machine learning is a form of artificial intelligence in which computers learn from experiences without scientists having to program them overtly.
An automatic learning system looks for patterns in a known set of data and decides which models identify specific characteristics. Once you have “learned” what these functions are, they can reliably recognize you in a new dataset.
The researchers added another program to the system to expand their ability to examine semantics. Previous studies have limited this analysis to the degree of semantic coherence, which evaluates how people use words from one sentence to another.
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