Computer technology known as machine learning
is up to 93% accurate in correctly classifying a suicidal person
and 85% accurate in identifying a person who is suicidal, has a
mental illness but is not suicidal, or neither, revealed a new study.
These results provide
strong evidence for using advanced technology as a decision-support tool
to help clinicians and caregivers identify and prevent suicidal
behavior, says John Pestian, professor in the divisions of
Biomedical Informatics and Psychiatry at Cincinnati Children's Hospital
Medical Center and the study's lead author.
‘Using a person's spoken or written words, new computer tools can identify with great accuracy whether that person is suicidal, mentally ill but not suicidal, or neither.’
"These computational approaches provide novel opportunities to apply
technological innovations in suicide care and prevention, and it surely
is needed," says Dr. Pestian. "When you look around health care
facilities, you see tremendous support from technology, but not so much
for those who care for mental illness. Only now are our algorithms
capable of supporting those caregivers. This methodology easily can be
extended to schools, shelters, youth clubs, juvenile justice centers,
and community centers, where earlier identification may help to reduce
suicide attempts and deaths."
The study is published in the journal Suicide and Life-Threatening Behavior
, a leading journal for suicide research.
Dr. Pestian and his colleagues enrolled 379 patients in the study
between Oct. 2013 and March 2015 from emergency departments and
inpatient and outpatient centers at three sites. Those enrolled included
patients who were suicidal, were diagnosed as mentally ill and not
suicidal, or neither - serving as a control group.
Each patient completed standardized behavioral rating scales and
participated in a semi-structured interview answering five open-ended
questions to stimulate conversation, such as "Do you have hope?" "Are
you angry?" and "Does it hurt emotionally?"
The researchers extracted and analyzed verbal and non-verbal
language from the data. They then used machine learning algorithms to
classify the patients into one of the three groups. The results showed
that machine learning algorithms can tell the differences between the
groups with up to 93% accuracy. The scientists also noticed that
the control patients tended to laugh more during interviews, sigh less,
and express less anger, less emotional pain and more hope.