Artificial intelligence learns to spot suicidal trends in brain scans

0
208

[ad_1]


brain
A
person competes in the Brain-Computer Interface Race (BCI) at the
Cybathlon Championships in Kloten, Switzerland, October 8,
2016.


Arnd
Wiegmann/Reuters



  • Clinicians normally have few tools to identify
    suicidal people at risk.
  • A new machine-learning technique using words could help
    identify those suffering from suicidal thoughts.

Suicide is the second-leading cause of death among young
people between the ages of 15 and 34 in the United States, and
clinicians have limited tools to identify those at risk. A new
machine-learning technique documented in a paper published today
in Nature Human Behaviour (PDF) could help
identify those suffering from suicidal thoughts.

Researchers looked at 34 young adults, evenly split between
suicidal participants and a control group. Each subject went
through a functional magnetic resonance imaging (fMRI) and were
presented with three lists of 10 words. All the words were
related to suicide (words like “death,” “distressed,” or
“fatal”), positive effects (“carefree,” “kindness,” “innocence”),
or negative effects (“boredom,” “evil,” “guilty”). The
researchers also used previously mapped neural signatures that
show the brain patterns of emotions like “shame” and “anger.”

Five brain locations, along with six of the words, were found to
be the best markers to distinguish the suicidal patients from the
controls. Using just those locations and words, the researchers
trained a machine-learning classifier that was able to correctly
identify 15 of the 17 suicidal patients and 16 of 17 control
subjects.

The researchers then divided the suicidal patients into two
groups, those that had attempted suicide (nine people) and those
that had not (eight people), and trained a new classifier that
was able to correctly identify 16 of the 17 patients.


mri brain scan
An MRI brain
scan.


Vimeo/JonO


The results showed that healthy patients and those with suicidal
thoughts showed markedly different reactions to words. For
example, when the suicidal participants were shown the word
“death,” the “shame” area of their brain lit up more than it did
in the control group. Likewise, “trouble” also evoked more
activity in the “sadness” area.

This is just the latest effort aimed at bringing AI into
psychiatry. Researchers are working on machine-learning
projects that span from analyzing MRIs to predict major
depressive disorder to picking out PTSD from people’s speech
patterns.

Earlier this year, Wired wrote about researchers who
built a system that can sift through health records to flag
someone at risk of committing suicide, with between 80 and 90
percent accuracy. Facebook is using text mining to identify users
at risk of suicide or self-harm and then pointing them to mental
health resources (see “Big Questions Around Facebook’s Suicide-Prevention
Tools
”).

Artificial intelligence has already made waves in the medical
field at large. There are algorithms so good at detecting tumors
and other problems in CT scans that Geoffrey Hinton, one of the foremost
researchers in deep learning, told the New Yorker that radiologists will
eventually be out of a job. Indeed, he said, “they should stop
training radiologists now.”

In this case, the research is more likely to inspire new
human-driven therapies than put a whole field’s worth of doctors
out of a job. The paper pointed out that identifying different
patterns and areas could suggest new regions to target for brain
stimulation techniques. Identifying particular emotional
responses to suicide-related terms could also be useful to
psychotherapists treating their patients.

[ad_2]

Source link

LEAVE A REPLY

This site uses Akismet to reduce spam. Learn how your comment data is processed.