A new assessment tool that leverages powerful artificial intelligence was able to predict whether participants exhibited suicidal thoughts and behaviors using a quick and simple combination of variables.
Developed by researchers at Northwestern University, the University of Cincinnati (UC), Aristotle University of Thessaloniki and Massachusetts General Hospital/Harvard School of Medicine, the system focuses on a simple picture-ranking task along with a small set of contextual/demographic variables rather than extensive psychological data.
The tool was on average 92% effective at predicting four variables related to suicidal thoughts and behaviors.
"A system that quantifies the judgment of reward and aversion provides a lens through which we may understand preference behavior," said first author Shamal Shashi Lalvani, a doctoral student at Northwestern University. "By using interpretable variables describing human behavior to predict suicidality, we open an avenue toward a more quantitative understanding of mental health and make connections to other disciplines such as behavioral economics."
The study, published today in the journal Nature Mental Health, concludes that a small set of behavioral and social measures play a key role in predicting suicidal thoughts and behaviors. The current work details the components of a tool that could be an app for medical professionals, hospitals or the military to provide assessment of who is most at risk of self-harm.
