By Aili McConnon
In late January, a 60-year-old women in northern Argentina
posted on Facebook: "This can't go on. From here I say
goodbye."
Within three hours, a medical team reached the woman and saved
her life -- thanks in part to advances in artificial
intelligence.
The post caught the attention of Facebook's AI system, which is
programmed to spot potential suicidal language. The system decided
it was an emergency and passed it along to moderators for review,
who then alerted authorities in Buenos Aires. Before long, first
responders were on the scene. (Facebook wouldn't comment on the
incident.)
"Artificial intelligence can be a very powerful tool," says
Enrique del Carril, the investigations director in the district
attorney's office in Buenos Aires. "We saved a woman far away in
remote Argentina before something terrible happened. That is
incredible."
Facebook's suicide-alert system is just one of many efforts to
use artificial intelligence to help identify people at risk for
suicide as early as possible. In these programs, researchers use
computers to comb through massive amounts of data, such as
electronic health records, social-media posts, and audio and video
recordings of patients, to find common threads among people who
attempted suicide. Then algorithms can start to predict which new
patients are more likely to be at risk.
Machine assistance
Machines wouldn't replace humans making diagnoses about suicidal
behavior. But these tools -- most of which are still experimental
-- could eventually help clinicians screen patients more quickly
and accurately, perhaps even while a doctor is still doing an
interview.
At the same time, some critics have raised concerns about the
privacy rights of patients as machines tap into their personal
data, as well as possible mistakes in how the information is
interpreted.
Using technology to detect suicidal behavior is part of a larger
effort to use AI to discover and treat a range of mental-health
issues including depression, schizophrenia and bipolar
disorder.
But suicide-detection research -- in the public and private
sectors -- is further along than other mental-health efforts. In
part, that's because suicide is on the rise, particularly among
teenagers. In 2006, one person in the U.S. committed suicide every
16 minutes, according to the Centers for Disease Control and
Prevention. A decade later, it was every 12 minutes. Plus,
traditional ways of predicting suicide have been found lacking. In
fact, a recent meta-analysis by Florida State University
researchers and others, published in the journal Psychological
Bulletin, found that the traditional approach of predicting
suicide, which includes doctors' assessments, was only slightly
better than random guessing.
By contrast, early tests of AI have shown markedly better
results. A follow-up study by several of the same researchers,
published in the journal Clinical Psychological Science last year,
used AI to analyze the medical records of nearly 16,000 general
hospital patients in Tennessee. The algorithms identified common
traits among suicidal patients -- such as a history of using
antidepressants and injuries with firearms -- and could predict
with 80% to 90% accuracy whether someone would attempt suicide in
the next two years.
The results show AI can "model complex interactions among many
risk factors" to decide who is most likely at risk, says Jessica
Ribeiro, psychology professor at Florida State University focused
on suicide prevention, and one of the researchers.
Other early tests combine analysis of medical records with
real-life data, such as what people say to their clinicians and how
they say it. John Pestian, director of computational medicine at
the Cincinnati Children's Hospital, took this approach in a study
published in 2016 in the journal Suicide and Life-Threatening
Behavior. Dr. Pestian looked at 379 people in one of three
categories: at serious risk for suicide; mentally ill but not
suicidal; and a control group. The subjects filled in surveys and
were interviewed and filmed.
An algorithm analyzed relevant patterns and could determine with
up to 93% accuracy who was actually in the suicidal group versus
someone who was mentally ill but not at risk, or a control. Among
other signs, the findings showed that mentally ill patients and
control patients tended to laugh more, sigh less, and express less
anger and emotional pain and more hope than those who exhibited
suicidal behavior. All of which, Dr. Pestian argues, could only be
gleaned from real-world interactions, not medical records.
Analyzing audio
Dr. Pestian has used his AI research to develop an app called
SAM that has been tested in Cincinnati schools and clinics. The app
records sessions between therapists and patients, then analyzes
linguistic and vocal factors to provide a real-time assessment of a
patient at risk for suicide.
Another system with a similar approach: Cogito's Companion,
developed by Cogito Corp. The system, which has been used with
about 500 veterans, analyzes data from users' phones, such as the
frequency with which they text or call and how much they have
traveled in a given week; users also record short audio diaries
that the system analyzes. Cogito says its app can detect depression
and suicidal behavior with more than 80% accuracy.
Some private-sector efforts to identify suicidal behavior are
already being used on a wide scale. In the past five years,
AI-powered virtual assistants such as Apple's Siri have started
directing users to the National Suicide Prevention Lifeline, and
offering to connect them, when they detect suicidal comments or
questions. That might include people using the word "suicide" or
saying something like "I want to jump off a bridge."
Facebook has been working on suicide prevention for more than 10
years, but faced criticism last year for not doing enough after
several users took their own lives and live-streamed the process.
In November 2017, Facebook said that it had started to use AI to
analyze people's posts and live streams in an effort to detect
suicidal thoughts, and that its AI system now prioritizes
particularly dangerous and urgent reports so that they are more
quickly addressed by moderators. The company says that over a month
in the fall of 2017, its AI system alerted first responders to
intervene in 100 cases of potential self-harm.
"We're always looking to improve our tools," says William
Nevius, a Facebook spokesman. "We know this is a new technology,
and we're always looking for additional ways to help people."
Potential roadblocks
But as companies get involved in the suicide-prevention efforts,
they face a host of ethical questions. For one, there's
transparency: Technology firms already have to deal with concerns
about the kinds of information they collect from users and what
they do with it, and those debates will likely become even more
heated as they handle sensitive mental-health information.
Legal and regulatory questions also arise, such as who assumes
responsibility if an AI system makes a false prediction. A wrong
guess, for instance, might leave an individual with a damaging data
trail suggesting they were suicidal.
In fact, such questions of privacy may plague any research into
suicide, some critics say. For medical AI systems to work well,
they need access to a wealth of data from a variety of patients,
but that can be tricky because of the perceived stigma of
mental-health disorders, says Siddarth Shah, an industry analyst at
research firm Frost & Sullivan. "How many people are going to
be OK with having sensitive mental-health information shared with
an algorithm?" he says.
Some efforts are under way to address that issue. For instance,
Qntfy, an Arlington, Va., company, is recruiting people to donate
data for study, and more than 2,200 people have done so to date.
Identifying information is scrubbed out of the data before it's
analyzed, the company says.
Finally, issues of nuance plague many AI efforts. Though AI may
recognize a word, it may not comprehend the context. "Saying 'I
hate this. I can't survive' is very different if you are saying it
to a doctor versus venting on social media," says Adam Miner, a
clinical psychologist and AI researcher at Stanford University.
Ms. McConnon is a writer in New York. Email reports@wsj.com.
(END) Dow Jones Newswires
February 23, 2018 11:02 ET (16:02 GMT)
Copyright (c) 2018 Dow Jones & Company, Inc.
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