0106 GMT December 12, 2019
Now, CU Boulder researchers are working to apply machine learning to psychiatry, with a speech-based mobile app that can categorize a patient's mental health status as well as or better than a human can, medicalxpress.com reported.
"We are not in any way trying to replace clinicians," said Peter Foltz, a research professor at the Institute of Cognitive Science and coauthor of a new paper in Schizophrenia Bulletin that lays out the promise and potential pitfalls of AI in psychiatry. "But we do believe we can create tools that will allow them to better monitor their patients."
Nearly one in five US adults lives with a mental illness, many in remote areas where access to psychiatrists or psychologists is scarce. Others can't afford to see a clinician frequently, don't have time or can't get in to see one.
Even when a patient does make it in for an occasional visit, therapists base their diagnosis and treatment plan largely on listening to a patient talk — an age-old method that can be subjective and unreliable, noted paper coauthor Brita Elvevåg, a cognitive neuroscientist at the University of Tromsø, Norway.
"Humans are not perfect. They can get distracted and sometimes miss out on subtle speech cues and warning signs," Elvevåg said. "Unfortunately, there is no objective blood test for mental health."
In pursuit of an AI version of that blood test, Elvevåg and Foltz teamed up to develop machine learning technology able to detect day-to-day changes in speech that hint at mental health decline.
For instance, sentences that don't follow a logical pattern can be a critical symptom in schizophrenia. Shifts in tone or pace can hint at mania or depression. And memory loss can be a sign of both cognitive and mental health problems.
"Language is a critical pathway to detecting patient mental states," said Foltz. "Using mobile devices and AI, we are able to track patients daily and monitor these subtle changes."