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. How Artificial Intelligence Can Help Burn Victims Computers Can Predict Schizophrenia Based on How a Person Talks In2015, 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.
In addition to the schizophrenia screener, an idea that earned Schwoebel an award from the American Psychiatric Association, NeuroLex is hoping to develop a tool for psychiatric patients who are already being treated in hospitals. Rather than trying to help diagnose a mental disorder from a single sample, the AI would examine a patient’s speech over time to track their progress. For Schwoebel, this work is personal: he thinks this approach may help solve problems his older brother faced in seeking treatment for schizophrenia. Before his first psychotic break, Schwoebel’s brother would send short, one-word responses, or make cryptic to references to going “there” or “here”—worrisome abnormalities that “all made sense” after his brother’s first psychotic episode, he said. According to Schwoebel, it took over 10 primary-care appointments before his brother was referred to a psychiatrist and eventually receive a diagnosis. After that, he was put on one medication that didn’t work for him, and then another. In the years it took to get Schwoebel’s brother diagnosed and on an effective regimen, he experienced three psychotic breaks. For cases that call for medication, this led Schwoebel to wonder how to get a person on the right prescription, and at the right dose, faster.