Harnessing deep neural networks to predict future self-harm based on clinical notes

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According to the American Foundation for Suicide Prevention, suicide is the 10th leading cause of death in the U.S., with over 1.4 million suicide attempts recorded in 2018. Although effective treatments are available for those at risk, clinicians do not have a reliable way of predicting which patients are likely to make a suicide attempt.

Researchers at the Medical University of South Carolina and University of South Florida report in JMIR Medical Informatics that they have taken important steps toward addressing the problem by creating an artificial intelligence algorithm that can automatically identify patients at high risk of intentional self-harm, based on the information in the clinical notes in the electronic health record.

The study was led by Jihad Obeid, M.D., co-director of the MUSC Biomedical Informatics Center, and Brian Bunnell, Ph.D., formerly at MUSC and currently an assistant professor in the Department of Psychiatry and Behavioral Neurosciences at the University of South Florida.

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