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As of today, suicide by firearm is by far the most lethal method of ending your own life, especially in the US. In 2020, approximately 48,000 US citizens died by suicide, more than 24,000 by firearm. With the easy access to firearms being a staple in US culture, what can be done to prevent those in risk from purchasing a weapon?
A new study from the Violence Prevention Research Program (VPRP) at UC Davis suggests that machine learning, a type of artificial intelligence, may help identify handgun purchasers who are at high risk of suicide. It also identified individual and community characteristics that are predictive of firearm suicide.
This first-of-a-kind study shows an algorithm can forecast the likelihood of firearm suicide using handgun purchasing data. The algorithm calculates risk factors such as older age, first time firearm purchase, purchase of a revolver, white race etc., to determine whether an individual is at high risk.
Sciencedaily.com has reported that previous research has indicated that the risk of suicide is particularly high immediately after purchase, which means that in order to prevent such incidents, law enforcement must act quick.
“While limiting access to firearms among individuals at increased risk for suicide presents a critical opportunity to save lives, accurately identifying those at risk remains a key challenge. Our results suggest the potential utility of handgun records in identifying high-risk individuals to aid suicide prevention,” said Hannah S. Laqueur, an assistant professor in the Department of Emergency Medicine and lead author of the study.
“Research has established a clear and strong association between firearm acquisition and ownership and firearm suicide risk, this study contributes to the growing evidence that computational methods can aid in the identification of high-risk groups and the development of targeted interventions,” Laqueur said.
Though this research was largely a “proof of concept”, the results suggest that utilizing firearm purchase records to identify high risk individuals is highly effective.