Durkheim Project leverages big data to prevent veteran suicides

By Thor Olavsrud , CIO |  Big Data

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Phase 1 of the Durkheim Project's research consisted of building a predictive model. Poulin (who at the time was co-director of the Dartmouth College Metalearning Working Group at Dartmouth Thayer School of Engineering) collaborated with researchers Paul Thompson, Thomas McAllister, MD and Laura Flashman, PhD, from the Geisel School of Medicine at Dartmouth and Brian Shiner, MD and Vince Watts, MD from the U.S. Department of Veterans Affairs.

Using a control group of veterans, the researchers focused on proving that text-mining methods could provide statistically significant predictions of suicidality.

"We needed to show that we have a medically efficacious classifier," Poulin says. "We achieved 65 percent accuracy. We're convinced that's a decent signal. It's not great, but it's consistent and we're going to build on that."

"The study we've begun with our research partners will build a rich knowledge base that eventually could enable timely interventions by mental health professionals," he adds.

Attivio and Cloudera Help Unify and Store Veterans' Social Media Data

While Phase 1 was underway, Durkheim Project partners Attivio and Cloudera were building a platform to allow the Durkheim Project to collect, store and analyze massive volumes of data.

"There's plenty of opportunity to use big data to make money," says Attivio CTO Sid Probstein. "Having the opportunity to use it for something like this is just fantastic."

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"Say a veteran who returns from a theater is having trouble dealing with things that happened, things they saw," he adds. "As they're in that state, we expect them to voice frustration, and to do so primarily in social media. On Twitter they might quote song lyrics or a poem. There are some common threads to this kind of expression."

"The system is not really trying to understand what the person is saying," he notes. "It really only is looking for patterns and to apply logic to that. It's not understanding that there's some negative expression. It's detecting the likelihood that negative expression is an indicator of someone that's at-risk."


Originally published on CIO |  Click here to read the original story.
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