Artificial intelligence can help to identify risk factors for suicide and self-harm, according to new research from UNSW Sydney.
Among adolescents in Australia, suicide is the leading cause of death and self-harm affects 18 per cent of those aged 14-17. Both have sadly become more common in this age group over the last decade.
Clinicians assess suicide and self-harm risk when a young person enters a health care setting, like a hospital, with potential suicidal or self-harming behaviour. Current risk assessment methods such as looking at past attempts can be unreliable, not taking into account the many other potential risk factors. Also, adolescents outside these healthcare settings fly under the radar.
Artificial intelligence (AI) is becoming more widely used in mental health to identify at-risk individuals. Machine learning (ML) models can process vast amounts of patient data, identifying potential risk factors and measuring how they can predict mental health issues including suicide and self-harm attempts.
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Researchers from UNSW, the Ingham Institute for Applied Medical Research and South Western Sydney Local Health District (SWSLHD) have developed ML models to predict the risk of suicide and self-harm attempts in adolescents. These models were more accurate than a standard approach, with previous suicide and self-harm attempts as the only risk factor.
The findings are published today in Psychiatry Research.
“Sometimes we need to digest and process a lot of information that would be beyond the ability of the clinician,” says senior author Dr Daniel Lin, who is a psychiatrist and mental health researcher affiliated with UNSW, the Ingham Institute and SWSLHD.
“That’s the reason we are tapping into machine learning algorithms.”
High prevalence of suicide and self-harm
The researchers used data from the Longitudinal Study of Australian Children, which has been collecting a range of data from children across the nation since 2004. Their analysis included 2809 of the study participants, split into a 14-15 years age group and a 16-17 years age group. The data came from questionnaires completed by the children, their carers and their school teachers.
Among the 2809 participants, 10.5 per cent reported an act of self-harm and 5.2 per cent reported attempting suicide at least once in the past 12 months.
“These behaviours are definitely under-reported, so the actual proportions are higher,” Dr Lin says.
Identifying risk factors
The researchers identified more than 4000 potential risk factors from the data, relating to areas like mental health, physical health, relationships with others, and the school and home environment. They used a random forest classification algorithm (an advanced ML technique) to identify which risk factors at age 14-15 were most predictive of suicide and self-harm attempts at age 16-17.
For suicide and self-harm, the most important risk factors were depressed feelings, emotional and behavioural difficulties, self-perceptions, and school and family dynamics. There were also unique factors specific to either suicide or self-harm.
“A unique predictor of suicide was lack of self-efficacy, when someone feels a lack of control over their environment and their future. And a unique predictor of self-harm was lack of emotional regulation,” Dr Lin says.
“It was surprising to us to see that previous attempts were not among the top risk factors.”
Source: University of New South Wales
Published on September 12, 2023