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People convey meaning by what they say as well as how they say it: Tone, word choice, and the length of a phrase are all crucial cues to understanding what’s going on in someone’s mind. When a psychiatrist or psychologist examines a person, they listen for these signals to get a sense of their wellbeing, drawing on past experience to guide their judgment. Researchers are now applying that same approach, with the help of machine learning, to diagnose people with mental disorders.

In 2015, a team of researchers developed an AI model that correctly predicted which members of a group of young people would develop psychosis—a major feature of schizophrenia—by analyzing transcripts of their speech. This model focused on tell-tale verbal tics of psychosis: short sentences, confusing, frequent use of words like “this,” “that,” and “a,” as well as a muddled sense of meaning from one sentence to the next.

Now, Jim Schwoebel, an engineer and CEO of NeuroLex Diagnostics, wants to build on that work to make a tool for primary-care doctors to screen their patients for schizophrenia. NeuroLex’s product would take a recording from a patient during the appointment via a smartphone or other device (Schwoebel has a prototype Amazon Alexa app) mounted out of sight on a nearby wall. Using the same model from the psychosis paper, the product would then search a transcript of the patient’s speech for linguistic clues. The AI would present its findings as a number—like a blood-pressure reading—that a psychiatrist could take into account when making a diagnosis. And as the algorithm is “trained” on more and more patients, that reading could better reflect a patient’s state of mind.

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The Atlantic

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