Want to know your mental
health status? There’s an app for that
By Lisa Marshall
November 12, 2019 -- Thanks to advances in
artificial intelligence, computers can now assist doctors in diagnosing disease
and help monitor patient sleep patterns and vital signs from hundreds of miles
away.
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.
“We are not in any way trying to replace
clinicians,” says Peter Foltz, a research professor at the Institute of
Cognitive Science and co-author 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 U.S. 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, notes paper co-author 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 says. “Unfortunately, there is no objective blood test for mental
health.”
Language a window into 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, disjointed
speech—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,” says Foltz. “Using mobile devices and AI, we
are able to track patients daily and monitor these subtle changes.”
The new mobile app asks patients to
answer a 5- to 10-minute series of questions by talking into their phone.
Among various other tasks, they’re
asked about their emotional state, asked to tell a short story, listen to a
story and repeat it and given a series of touch-and-swipe motor skills tests.
In collaboration with Chelsea Chandler,
a computer science graduate student at CU Boulder, and other colleagues, they
developed an AI system that assesses those speech samples, compares them to
previous samples by the same patient as well as the broader population and
rates the patient’s mental state.
In one recent study, the team asked
human clinicians to listen to speech samples of 225 participants—half with
severe psychiatric issues; half healthy volunteers—in rural Louisiana and
Northern Norway and assess them. They then compared those results to those of
the machine learning system.
“We found that the computer’s AI models
can be at least as accurate as clinicians,” says Foltz.
Their technology is not commercially
available yet. But he and his colleagues envision a day when such AI
systems could be in the room with a therapist and a patient to provide
additional data-driven insight, or serve as a remote-monitoring system for the
severely mentally ill.
If the app detected a worrisome change,
it could notify the patient’s doctor to check in.
“Patients often need to be
monitored with frequent clinical interviews by trained professionals to avoid
costly emergency care and unfortunate events,” says Foltz. “ But there are
simply not enough clinicians for that.”
Research call to action
Foltz previously helped develop and
commercialize an AI-based essay-grading technology which is now broadly
used to help educators do their job.
In their new paper, the researchers lay
out a call to action for larger studies to prove efficacy and earn public trust
before AI technology could be broadly brought into clinical practice for
psychiatry.
“The mystery around AI does not nurture
trustworthiness, which is critical when applying medical technology,” they
write. “Rather than looking for machine learning models to become the ultimate
decision-maker in medicine, we should leverage the things that machines do well
that are distinct from what humans do well.”
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