Study: Machine learning shows promise toward accurately identifying suicidal behavior

Digital tools using machine learning to analyze a person's spoken or written words could be instrumental in aiding mental health clinicians in assessments determining whether that person is suicidal, researchers have found. A new study published in the journal Suicide and Life-Threatening Behavior found machine learning is 93 percent accurate in correctly identifying a suicidal person, and is 85 percent accurate in determining differential diagnosis of mental illness. The study, led by researchers at the Cincinnati Children's Hospital Medical Center, looked at 379 patients who were recruited from three different sites – two academic medical centers and a rural community hospital. "Death by suicide demonstrates profound personal suffering and societal failure," writes lead author Dr. John Pestian, who is also a professor of biomedical informatics and psychiatry at Cincinnati Children's. "While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers."