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Computing—Assessing veteran suicide risk

  • ORNL’s project for the Department of Veterans Affairs bridges computing prowess and VA health data to speed up suicide risk screenings for United States veterans. Credit: Carlos Jones/Oak Ridge National Laboratory, U.S. Dept. of Energy

  • Oak Ridge National Laboratory’s Jeremy Cohen, Edmon Begoli and Joshua Arnold discuss their work on a collaborative project to more quickly screen Department of Veterans Affairs' health data and identify those who may be at risk. Credit: Carlos Jones/Oak Ridge National Laboratory, U.S. Dept. of Energy

  • Oak Ridge National Laboratory’s Joshua Arnold worked on the U.S. Veterans Administration’s medication possession ratio algorithm to cut its processing time down to minutes from what would have taken 75 hours. Credit: Carlos Jones/Oak Ridge National Laboratory, U.S. Dept. of Energy

  • ORNL’s project for the Department of Veterans Affairs bridges computing prowess and VA health data to speed up suicide risk screenings for United States veterans. Credit: Carlos Jones/Oak Ridge National Laboratory, U.S. Dept. of Energy

  • Oak Ridge National Laboratory’s Jeremy Cohen, Edmon Begoli and Joshua Arnold discuss their work on a collaborative project to more quickly screen Department of Veterans Affairs' health data and identify those who may be at risk. Credit: Carlos Jones/Oak Ridge National Laboratory, U.S. Dept. of Energy

  • Oak Ridge National Laboratory’s Joshua Arnold worked on the U.S. Veterans Administration’s medication possession ratio algorithm to cut its processing time down to minutes from what would have taken 75 hours. Credit: Carlos Jones/Oak Ridge National Laboratory, U.S. Dept. of Energy

Topics: Supercomputing Computer Science

In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly. The model, called the medication possession ratio algorithm, creates individualized summaries of veterans’ medication patterns. It helps clinicians pinpoint veterans with inconsistent medication usage who may have a higher risk of attempting suicide. With the accelerated model, “we can observe and reach a much larger population that’s potentially at risk—and look at even more risk factors,” ORNL’s Edmon Begoli said. The sped-up version of the model can assess the behavior patterns of nine million veterans in only 15 minutes. “The potential to provide far greater predictive services is there,” he added.

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