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.
The possibility of bolstering a mental health clinician’s judgment with a more “objective,” “quantitative” assessment appeals to the Massachusetts General Hospital psychiatrist Arshya Vahabzadeh, who has served as a mentor for a start-up accelerator Schwoebel cofounded. “Schizophrenia refers to a cluster of observable or elicitable symptoms” rather than a catchall diagnosis, he said. With a large enough data set, an AI might be able to split diagnoses like schizophrenia into sharper, more helpful categories based off the common patterns it perceives among patients. “I think the data will help us subtype some of these conditions in ways we couldn’t do before.”
– Joseph Frankel