An Oak Ridge Team recently sped up a computer model that helps predict veteran suicide risk. (Image credit: Carlos Jones, ORNL)
Submitted by Oak Ridge National Laboratory
More than 6,000 veterans died by suicide in 2016, and from 2005 to 2016, the rate of veteran suicides in the United States increased by more than 25 percent.
Suicide prevention is the highest priority for the U.S. Department of Veterans Affairs—so much so that in recent years, the VA has started using predictive models and advanced informatics (the study of information processing; computer science) to identify at-risk veterans.
One model of this type is called the medication possession ratio algorithm. It creates individualized summaries of veterans’ medication patterns, such as which medications a veteran is prescribed and how often those prescriptions are filled. The model helps clinicians pinpoint veterans with inconsistent medication usage patterns. These veterans are known to have a higher risk of attempting suicide in the next month.
In a collaborative project with the VA, a team at the U.S. Department of Energy’s Oak Ridge National Laboratory has taken the model and engineered the expanded version of it to run 300 times faster, gaining an unprecedented acceleration that might have a profound effect on the VA’s ability to reach susceptible veterans quickly. [Read more…]
