Researchers have used a machine learning algorithm to identify the top factors that can predict an adolescent’s risk of self-harm and attempting suicide. They say their model is more accurate than existing risk predictors and could be used to provide individualized care to vulnerable patients.
Adolescence is a critical formative period. Physical, emotional, and social changes can make adolescents vulnerable to mental health problems, including suicide attempts and self-harm. According to the Australian Institute of Health and Welfare (AIHW), suicide is the leading cause of death amongst Australians aged 15 to 24. In the US, the Centers for Disease Control and Prevention (CDC) lists it as the second leading cause for 10-to-14-year-olds.
The standard approach for predicting suicide or self-harm relies on past suicide or self-harm attempts as the only risk factor, which can be unreliable. Now, researchers led by the University of New South Wales Sydney have used machine learning (ML) to accurately identify the top factors that place adolescents at increased risk of suicide and self-harm.
“Sometimes we need to digest and process a lot of information that would be beyond the ability of the clinician,” said Ping-I Daniel Lin, corresponding author of the study. “That’s the reason we are tapping into machine learning algorithms.”