AI could help with suicide prediction, prevention

Around nine Australians take their own life each day, with suicide the leading cause of death for Australians aged 15–44. Some estimates suggest that suicide attempts occur up to 30 times as often as deaths.

Identifying individuals at risk of suicide is essential for preventing and managing suicidal behaviours. However, risk prediction is difficult.

In emergency departments, risk assessment tools such as questionnaires and rating scales are commonly used by clinicians to identify patients at elevated risk of suicide. Those classified as low risk are typically discharged.

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 were 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.

Karen Kusuma, a UNSW Sydney PhD candidate in psychiatry, and a team of researchers at the Black Dog Institute recently investigated the evidence base of machine learning models and their ability to predict future suicidal behaviours 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 machine learning models outperformed the benchmarks set previously by traditional clinical, theoretical and statistical suicide risk prediction models. They correctly predicted 66% of people who would experience a suicide outcome and correctly predicted 87% of people who would not experience a suicide outcome.

Kusuma said there is a need for more innovation in suicidology and a re-evaluation of standard suicide risk prediction models. Efforts to improve risk prediction have led to her research using artificial intelligence (AI) to develop suicide risk algorithms.

The strict assumptions of traditional statistical models do not bind machine learning models. Instead, they can be flexibly applied to large datasets to model complex relationships between many risk factors and suicidal outcomes. They can also incorporate responsive data sources, including social media, to identify peaks of suicide risk and flag times where interventions are most needed. “Over time, machine learning models could be configured to take in more complex and larger data to better identify patterns associated with suicide risk,” Kusuma said.

If you or someone you know is struggling with mental health or suicidal thoughts, call Lifeline on 131 114 or Beyond Blue on 1300 224 636.

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