Researchers have used artificial intelligence to map which factors are particularly related to young people’s suicide attempts.

The number of suicide attempts is rising at an alarming rate: In 59 low- and middle-income countries, one in six young people have attempted suicide, according to studies.

This worrying development calls for stronger preventive efforts, which is at the center of the new study, published in the Journal of Youth and Adolescence.

“It is crucial to identify the life circumstances that increase suicide risk among young people,” says Milan Obaidi, associate professor at the psychology department at the University of Copenhagen. “Unfortunately, current methods for estimating risk factors are close to useless—thus, authorities cannot identify the people who are at risk.”

The researchers are left with a clear picture of the main risk factors:

“Recent self-harm is the most important indication of risk for suicide attempts. In addition, we found five other risk factors: internalizing problems such as anxiety and depression, sleep problems, eating disorders, pessimism about future prospects, and victimization,” Obaidi says.

There have been previous attempts to use machine learning to localize suicide risk, but these have had significant shortcomings.

“Among other things, the interaction of protective factors and risk factors has been overlooked. And previous studies have neglected to include established theories on suicidal behavior and instead used purely algorithmic risk estimation,” Obaidi says.

The researchers’ machine learning model is the most accurate of its kind to date. In other words, their model can identify which young people are at risk better than anyone else.

Read the full article about AI flags teenage suicide by Simon Knokgaard at Futurity.