Researchers have developed a computer program that can identify an individual's suicidal thoughts with over 90 percent accuracy, a recent study showed.
The study, published online Monday in the journal Nature Human Behavior, used a machine-learning algorithm to identify two groups of people -- one group consisting of 17 individuals with known suicidal thoughts and one control group consisting of 17 neurotypical individuals.
While in a brain scanner, the participants were presented with positive, negative and death-related concepts, including death, cruelty, trouble and carefree.
Researchers said the algorithm they developed was 91 percent accurate in identifying whether a person was from the suicidal or control group, and 94 percent accurate in identifying people who had attempted suicide from individuals who only thought about it.
"People with suicidal thoughts experience different emotions when they think about some of the test concepts. For example, the concept of 'death' evoked more shame and more sadness in the group that thought about suicide," said Marcel Just, an author of the study and a psychology professor in Carnegie Mellon University's Dietrich College of Humanities and Social Sciences.
"This extra bit of understanding may suggest an avenue to treatment that attempts to change the emotional response to certain concepts," Just told the university in an interview.
Researchers said they hope the study can offer a new approach to assessing psychiatric disorders.
"Our latest work is unique insofar as it identifies concept alterations that are associated with suicidal ideation and behavior, using machine-learning algorithms to assess the neural representation of specific concepts related to suicide," Just said.
"This gives us a window into the brain and mind, shedding light on how suicidal individuals think about suicide and emotion related concepts," he added.
