Machine learning can spot patterns in people's use of language that even doctors who have undergone training to diagnose and treat those at risk of psychosis may not notice.
"Trying to hear these subtleties in conversations with people is like trying to see microscopic germs with your eyes," explains first study author Neguine Rezaii, a fellow in the Department of Neurology at Harvard Medical School.
However, it is possible to use machine learning to find certain subtle patterns hiding in people's language. "It's like a microscope for warning signs of psychosis," she adds.
Rezaii began working on the study while she was a resident in the Department of Psychiatry and Behavioral Sciences at Emory University School of Medicine.
Psychosis is a state of mind in which it can be difficult to tell the difference between what is real and what is not.
When a person enters this state of mind, doctors call it a psychotic episode. During such an episode, people experience disturbed perceptions and thoughts. Delusions and hallucinations are common symptoms of psychosis.
During a psychotic episode, a person may display inappropriate behavior or talk incoherently. In addition, they may experience sleep disruption and become socially withdrawn, depressed, and anxious.
In the United States, about 3% of people will experience a period of psychosis during their lifetime, according to figures from the National Institute of Mental Health, which is one of the National Institutes of Health (NIH).
Improving early diagnosis of psychosis risk
Psychosis is a hallmark of schizophrenia and other severe long-term mental health conditions.
The warning signs of psychosis usually begin during the mid to late teenage years with a cluster of psychosis symptoms that doctors describe as prodromal syndrome.
Around 25–30% of teens who develop prodromal syndrome will develop a psychotic illness such as schizophrenia.
From iMachine learning can spot patterns in people's use of language that even doctors who have undergone training to diagnose and treat those at risk of psychosis may not notice.
"Trying to hear these subtleties in conversations with people is like trying to see microscopic germs with your eyes," explains first study author Neguine Rezaii, a fellow in the Department of Neurology at Harvard Medical School.
However, it is possible to use machine learning to find certain subtle patterns hiding in people's language. "It's like a microscope for warning signs of psychosis," she adds.
Rezaii began working on the study while she was a resident in the Department of Psychiatry and Behavioral Sciences at Emory University School of Medicine.
Psychosis is a state of mind in which it can be difficult to tell the difference between what is real and what is not.
When a person enters this state of mind, doctors call it a psychotic episode. During such an episode, people experience disturbed perceptions and thoughts. Delusions and hallucinations are common symptoms of psychosis.
During a psychotic episode, a person may display inappropriate behavior or talk incoherently. In addition, they may experience sleep disruption and become socially withdrawn, depressed, and anxious.
In the United States, about 3% of people will experience a period of psychosis during their lifetime, according to figures from the National Institute of Mental Health, which is one of the National Institutes of Health (NIH).
Improving early diagnosis of psychosis risk
Psychosis is a hallmark of schizophrenia and other severe long-term mental health conditions.
The warning signs of psychosis usually begin during the mid to late teenage years with a cluster of psychosis symptoms that doctors describe as prodromal syndrome.
Around 25–30% of teens who develop prodromal syndrome will develop a psychotic illness such as schizophrenia.nterviews and tests of cognitive ability, doctors with the appropriate training can usually predict which people with prodromal syndrome will go on to develop psychosis with an accuracy of around 80%.
Scientists are trying various approaches to improve this prediction rate and make the diagnostic process more accurate and straightforward. Machine learning is one of these approaches.
Prof. Wolff and his team began their study by getting their machine-learning system to identify the language norms of everyday conversation.
They fed the system online conversations from 30,000 users of Reddit. Reddit is an online news, content rating, and discussion platform where registered users can converse about various topics.
The team used Word2Vec software to analyze individual words in the conversation. The software maps words so that those that have similar meanings are near each other in "semantic space," while those that have very dissimilar meanings are far away from each other.
The researchers added another program to the system to extend its ability to analyze semantics. Previous studies have confined this analysis to measuring semantic coherence, which looks at how people use words across sentences.
However, semantic density goes a step further and also assesses how people organize their words into sentences. The team suggests that this is a better indicator of the mental processes that people use to form sentences.
After training the machine-learning system to establish a "normal baseline," the team then fed it the conversations from diagnostic interviews of 40 participants in the North American Prodrome Longitudinal Study (NAPLS).
NAPLS is a multisite, 14-year project that aims to improve doctors' ability to diagnose young people who might be at risk of developing psychosis and to understand the reasons.
The team then compared the machine-learning analysis of the NAPLS conversations with the baseline data. They also compared it with follow-up data that showed which participants went on to develop psychosis.
The results revealed that participants who later developed psychosis tended to use more sound-related words than the baseline, and they also used words of similar meaning more frequently.
"If we can identify individuals who are at risk earlier and use preventive interventions," explains co-author Prof. Elaine Walker, "we might be able to reverse the deficits."
"There are good data showing that treatments like cognitive-behavioral therapy can delay onset and perhaps even reduce the occurrence of psychosis," she adds.
The team is now putting together more extensive collections of data and plans to test the new machine-learning technique with other brain and psychiatric conditions, such as dementia.
"This research is interesting not just for its potential to reveal more about mental illness but for understanding how the mind works — how it puts ideas together."
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