AI may Improve Suicide Prevention in the Future

The loss of any life can be devastating, but the loss of a life from suicide is especially tragic.

Around nine Australians take their own life 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 PhD 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 Centre for Big Data Research in Health 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 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 behaviours. 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, of those who received a formal suicide risk assessment, 75 per cent 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.

Despite the shortage of evidence in favour of traditional suicide risk assessments, their administration remains a standard practice in healthcare settings to determine a patient’s level of care and support. Those identified as having a high risk typically receive the highest level of care, while those identified as low risk are discharged.

"Using this approach, unfortunately, the high-level interventions aren't being given to the people who really need help. So we must look to reform the process and explore ways we can improve suicide prevention," Ms Kusuma says.

Machine learning suicide screening

Ms Kusuma says 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.

"Having AI that could take in a lot more data than a clinician would be able to better recognise which patterns are associated with suicide risk," Ms Kusuma says.

In the meta-analysis study, machine learning models outperformed the benchmarks set previously by traditional clinical, theoretical and statistical suicide risk prediction models. They correctly predicted 66 per cent of people who would experience a suicide outcome and correctly predicted 87 per cent of people who would not experience a suicide outcome.

"Machine learning models can predict suicide deaths well relative to traditional prediction models and could become an efficient and effective alternative to conventional risk assessments," Ms Kusuma says.

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," Ms Kusuma says.

The use of machine learning algorithms to predict suicide-related outcomes is still an emerging research area, with 80 per cent of the identified studies published in the past five years. Ms Kusuma says future research will also help address the risk of aggregation bias found in algorithmic models to date.

"More research is necessary to improve and validate these algorithms, which will then help progress the application of machine learning in suicidology," Ms Kusuma says. "While we're still a way off implementation in a clinical setting, research suggests this is a promising avenue for improving suicide risk screening accuracy in the future."

Karen Kusuma, Mark Larsen, Juan C Quiroz, Malcolm Gillies, Alexander Burnett, Jiahui Qian, Michelle Torok.
The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: A meta-analysis and systematic review.
Journal of Psychiatric Research, Volume 155, 2022. doi: 10.1016/j.jpsychires.2022.09.050

Most Popular Now

AI-Powered CRISPR could Lead to Faster G…

Stanford Medicine researchers have developed an artificial intelligence (AI) tool to help scientists better plan gene-editing experiments. The technology, CRISPR-GPT, acts as a gene-editing “copilot” supported by AI to help...

Groundbreaking AI Aims to Speed Lifesavi…

To solve a problem, we have to see it clearly. Whether it’s an infection by a novel virus or memory-stealing plaques forming in the brains of Alzheimer’s patients, visualizing disease processes...

AI Spots Hidden Signs of Depression in S…

Depression is one of the most common mental health challenges, but its early signs are often overlooked. It is often linked to reduced facial expressivity. However, whether mild depression or...

ChatGPT 4o Therapeutic Chatbot 'Ama…

One of the first randomized controlled trials assessing the effectiveness of a large language model (LLM) chatbot 'Amanda' for relationship support shows that a single session of chatbot therapy...

AI Tools Help Predict Severe Asthma Risk…

Mayo Clinic researchers have developed artificial intelligence (AI) tools that help identify which children with asthma face the highest risk of serious asthma exacerbation and acute respiratory infections. The study...

AI Model Forecasts Disease Risk Decades …

Imagine a future where your medical history could help predict what health conditions you might face in the next two decades. Researchers have developed a generative AI model that uses...

AI Distinguishes Glioblastoma from Look-…

A Harvard Medical School–led research team has developed an AI tool that can reliably tell apart two look-alike cancers found in the brain but with different origins, behaviors, and treatments. The...

AI Model Indicates Four out of Ten Breas…

A project at Lund University in Sweden has trained an AI model to identify breast cancer patients who could be spared from axillary surgery. The model analyses previously unutilised information...

Smart Device Uses AI and Bioelectronics …

As a wound heals, it goes through several stages: clotting to stop bleeding, immune system response, scabbing, and scarring. A wearable device called "a-Heal," designed by engineers at the University...

Overcoming the AI Applicability Crisis a…

Opinion Article by Harry Lykostratis, Chief Executive, Open Medical. The government’s 10 Year Health Plan makes a lot of the potential of AI-software to support clinical decision making, improve productivity, and...

Dartford and Gravesham Implements Clinis…

Dartford and Gravesham NHS Trust has taken a significant step towards a more digital future by rolling out electronic test ordering using Clinisys ICE. The trust deployed the order communications...