Around nine Australians take their own lives each day, and it is the leading cause of death for Australians aged 15-44. Suicide attempts are more common, with some estimates stating that they occur up to 30 times as often as deaths.
"Suicide has large effects when it happens. It impacts many people and has far-reaching consequences for family, friends and communities," says Karen Kusuma, a UNSW Sydney Ph.D. Candidate in psychiatry at the Black Dog Institute, who investigates suicide prevention in adolescents.
Ms. Kusuma and a team of researchers from the Black Dog Institute and the Center for Big Data Research in Health recently investigated the evidence base of machine learning models and their ability to predict future suicidal behaviors and thoughts. They evaluated the performance of 54 machine learning algorithms previously developed by researchers to predict suicide-related outcomes of ideation, attempt and death.
The meta-analysis, published in the Journal of Psychiatric Research, found that machine learning models outperformed traditional risk prediction models in predicting suicide-related outcomes, which have traditionally performed poorly.
"Overall, the findings show there is a preliminary but compelling evidence base that machine learning can be used to predict future suicide-related outcomes with very good performance," Ms. Kusuma says.
Traditional suicide risk assessment models
Identifying individuals at risk of suicide is essential for preventing and managing suicidal behaviors. However, risk prediction is difficult.
In emergency departments (EDs), risk assessment tools such as questionnaires and rating scales are commonly used by clinicians to identify patients at elevated risk of suicide. However, evidence suggests they are ineffective in accurately predicting suicide risk in practice.
"While there are some common factors shown to be associated with suicide attempts, what the risks look like for one person may look very different in another," Ms. Kusuma says. "But suicide is complex, with many dynamic factors that make it difficult to assess a risk profile using this assessment process."
A post-mortem analysis of people who died by suicide in Queensland found that of those who received a formal suicide risk assessment, 75% were classified as low-risk, and none was classified as high-risk. Previous research examining the past 50 years of quantitative suicide risk prediction models also found they were only slightly better than chance in predicting future suicide risk.
"Suicide is a leading cause of years of life lost in many parts of the world, including Australia. But the way suicide risk assessment is done hasn't developed recently, and we haven't seen substantial decreases in suicide deaths. In some years, we've seen increases," Ms. Kusuma says.
