Could a Machine Identify Suicidal Thoughts?
Despite decades of effort, it has proven frustratingly difficult to predict who is most at risk of dying by suicide. Relying on patients to reveal their intentions doesn’t work. Nearly 80 percent of those who die by suicide hide their suicidal thoughts from doctors and therapists during their last visits. Yet suicide rates are increasing among middle-aged Americans and it is the second-leading cause of death for young people. That’s why researchers have been urgently searching for a reliable biological predictor of suicidal thoughts and behavior.
A report published this week in Nature Human Behaviour suggests an intriguing new possibility. The study combined neural imaging with machine learning to explore whether the brains of suicidal people respond differently to positive and negative words related to life and death. “It turns out they do,” says co-author Matthew Nock, a clinical psychologist at Harvard University. “We can predict with a pretty surprising degree of accuracy who’s had thoughts of suicide and who hasn’t—and even among those with thoughts of suicide, who has made an attempt and who hasn’t.”
Although the study was small, the findings are remarkable, says Barry Horwitz, chief of the Brain Imaging and Modeling Section at the National Institute on Deafness and Other Communication Disorders, who wrote a commentary that accompanies the study. Horwitz was particularly impressed by the technique’s ability to correctly classify the nine out of 17 suicidal subjects who had made attempts at taking their own lives, a group experts say are as difficult to find as needles in a haystack. “It’s hard to imagine any other method or risk factor allowing you to make that kind of distinction,” Horwitz says.
Earlier this year, machine learning was reported to detect suicide risk based on health records with 80 to 90 percent accuracy—considered an encouraging result. But this new study stands out because it reveals a potential biological marker for suicidal thinking. “It’s not just a reported behavior,” says co-author Marcel Just, a cognitive neuroscientist at Carnegie Mellon University, “we get the actual thoughts they have about suicide, and we see how they’re altered.”
The study joined two separate lines of research. Nock had previously used implicit association tests to determine suicide risk. For example, he paired words related to death and life with “like me” and “not like me.” Suicidal people were about three times more likely to respond more quickly than controls when death and me were paired. That result has been replicated repeatedly, and has proven to be a relatively strong predictor of suicidal thoughts and behavior compared to other approaches such as medical assessments.