The Sound of Psychosis

Reading time
1 min read
Processes involved in vector unpacking

Neuroscientists developed a system that, they say, can detect subtle signs of psychosis in conversational speech.

What’s new: Researchers at Emory School of Medicine used machine learning to predict the onset of schizophrenia in a high-risk population with 80 percent accuracy. Their results were published in the journal npj Schizophrenia.

How it works: The researchers trained their neural network on thousands of conversations from Reddit to establish conversational norms and organize word vectors by usage.

  • They fed the system transcripts of interviews between young people at high risk of psychosis and their doctors, labeled to indicate speakers who eventually developed schizophrenia.
  • Among patients who eventually developed the disease, the researchers found higher rates of two verbal tics: words related to sound (such as loud, hush, and whisper) and use of multiple words with similar meanings.

Why It matters: Schizophrenia is a devastating condition that has no cure, but early detection can help people seek treatment before it becomes overwhelming.

Takeaway: Methods exist to identify warning signs of schizophrenia in patients as young as 17, but only around 30 percent of these people eventually develop the disorder. Machine-learning techniques could help doctors spot the patients who really need help.


Subscribe to The Batch

Stay updated with weekly AI News and Insights delivered to your inbox