Using Artificial Intelligence To Help Prevent Suicide

The loss of any life is devastating, but the loss of life due to suicide is exceptionally saddening.

Suicide is the primary cause of mortality for Australians aged 15 to 44, taking the lives of almost nine people daily. According to some estimates, suicide attempts happen up to 30 times more often than fatalities.

“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 University of New South Wales Ph.D. candidate in psychiatry at the Black Dog Institute, who investigates suicide prevention in adolescents.

Recent research conducted by Ms. Kusuma and a group of scientists from the Black Dog Institute and the Centre for Big Data Research in Health investigated the evidence supporting machine learning models’ ability to predict potential suicidal behaviors and thoughts. They evaluated the efficacy of 54 machine learning algorithms that were previously created 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 conventional risk prediction models in predicting suicide-related outcomes, which had 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.

In order to prevent and manage suicidal behaviors, it is crucial to identify those who are at risk of suicide. However, predicting risk is challenging.

In emergency departments (EDs), doctors often employ risk assessment tools, such as questionnaires and rating scales, to pinpoint patients who are at a high risk of suicide. Evidence, however, indicates that they are ineffective in accurately determining 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, of those who received a formal suicide risk assessment, 75 percent 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.