USU Study Leverages Machine Learning to Improve Suicide Risk Detection in Soldiers

Suicidal behavior remains a significant concern within the U.S. Army, presenting a persistent challenge for military mental health professionals in accurately identifying soldiers at risk. A recent Uniformed Services University (USU) study, however, offers a promising new approach, utilizing machine learning to better predict suicide attempts among U.S. Army soldiers using data available from their Periodic Health Assessment (PHA). This vital research was conducted and published in the Nature Mental Health journal by Dr. James Naifeh, assistant scientific director at the Center for the Study of Traumatic Stress (CSTS) and a research associate professor in the Department of Psychiatry at USU’s School of Medicine, along with colleagues from USU and Harvard Medical School.