TL;DR:
- AI models can enhance suicide prevention among children, outperforming traditional methods.
- A study by UCLA Health shows machine learning can detect self-injury thoughts or behavior better.
- Suicide is a significant cause of death for young people in Europe and the US.
- Current systems miss a substantial number of at-risk children.
- Machine-learning models based on medical data greatly improve detection rates.
Main AI News:
In recent years, the potential of artificial intelligence (AI) to transform various industries has been evident, and now, it seems AI is making a significant impact in an area of utmost importance: suicide prevention among children. In a peer-reviewed study conducted by researchers from UCLA Health and published in the prestigious journal JMIR Mental Health, groundbreaking insights were revealed, indicating that machine learning models can greatly enhance the detection of self-injurious thoughts and behaviors in young individuals compared to the current data systems utilized by healthcare providers.
The study’s context is particularly critical, as suicide remains a leading cause of death among young people in Europe, with a staggering nine million children between the ages of 10 and 19 living with mental disorders, where anxiety and depression account for more than half of all cases. Similarly, in the United States, approximately 20 million young people are diagnosed with mental health disorders, according to the US Department of Health and Human Services.
To understand the limitations of existing systems for evaluating the mental health of children seeking emergency care, the UCLA Health researchers meticulously reviewed clinical notes from 600 emergency department visits made by children aged between 10 and 17. Shockingly, the study revealed that the current clinical notes-based evaluation system missed 29% of children with self-injurious thoughts or behaviors, while health specialists’ statements, intended to flag at-risk patients, failed to identify 54% of patients exhibiting such signs.
A primary reason behind these shortcomings is that children often do not explicitly report their suicidal thoughts and behaviors during their initial visit to the emergency department. Even when the two systems were combined, they still missed 22% of children at risk. The study further highlighted disparities in detection, as boys were more likely to be overlooked than girls and Black and Latino youth faced higher chances of being left out than their white counterparts.
Enter machine learning, the game-changer. The UCLA Health researchers developed three machine-learning models that harnessed a wealth of data, including previous medical care, medication history, patient locations, and lab test results, to estimate suicide-related thoughts and self-injurious behaviors. The results were astounding: all three machine-learning models outperformed the traditional methods in identifying at-risk children.
Lead author of the study, Juliet Edgcomb, emphasized the significance of this development, stating, “Our ability to anticipate which children may have suicidal thoughts or behaviors in the future is not great – a key reason is our field jumped to prediction rather than pausing to figure out if we are actually systematically detecting everyone who is coming in for suicide-related care. We sought to understand if we can first get better at detection.”
While the machine-learning models did show a higher chance of producing false positives – that is, identifying kids at risk when they are not – Edgcomb pointed out that this is preferable to the alternative of missing out on potential cases entirely.
Conclusion:
The study conducted by UCLA Health demonstrates that AI-powered machine learning models have the potential to revolutionize youth suicide prevention. By significantly improving the detection of self-injurious thoughts and behaviors among children seeking emergency care, these advanced algorithms can provide healthcare providers with a vital tool to save young lives. As AI continues to advance, its impact on mental health care will likely shape a burgeoning market for innovative and data-driven solutions. Organizations within the healthcare industry should keep a close eye on AI developments to stay competitive and deliver more effective and targeted services to address the critical issue of youth mental health.
