Berkeley uses deep learning to address suicide risks among veterans

Researchers from Berkeley Lab at the University of California have been applying deep learning and analytics to electronic health record data to help the Veterans Administration tackle medical and psychological challenges of returning service members.

WHY IT MATTERS Working with a publicly available dataset containing medical record information on about 40,000 patients from one Boston hospital intensive care unit, researchers searched for patterns that might point to suicide risk.

The goal is to improve identification of patients at risk for suicide through new patient-specific algorithms that produce tailored and dynamic suicide risk scores, and make those resources available to VA caregivers and patients.

Suicide is the 10th leading cause of death in the U.S., and it is significantly higher in the veteran population, with 20-22 deaths per day – an alarming statistic the VA’s Million Veteran Program Suicide Prevention Exemplar project is designed to help address.

Early efforts focused primarily on finding patterns in a diverse and complex pool of data, such as building a deep learning network that can distinguish and classify patients at high risk for suicide from discharge notes and physicians’ notes found in these datasets.