The Veterans Administration is funding the Durkheim Project, an effort to use text and sentiment analysis to predict veterans suicide risk.
That’s where computer scientist Chris Poulin and a semantics-based prediction tool enter the picture. Poulin and his company, Patterns & Predictions, had developed a commercial Bayesian analytics tool for predicting events—most notably financial events—based on historical analysis. “You have a stock that went bust on a certain date,” explains Poulin. “What were the forensic features that led up to that stock going bust?”
Ten years ago, as Poulin was launching the company, his best friend committed suicide. “He posted a suicide note—and what turned out to be pre-suicide notes—on social media,” says Poulin. As time went on, Poulin began to consider whether a similar event prediction model could parse the social media behavior of veterans to uncover those who might be about to harm themselves. Later, as a researcher at Dartmouth, Poulin partnered with Paul Thompson, an instructor at the university’s Geisel School of Medicine who specializes in computational linguistics and also lost a friend to suicide, and they took their pitch to the Pentagon. Three years ago, they were awarded a $1.7 million contract by the Defense Advanced Research Projects Agency (DARPA) to combine Thompson’s linguistics work with Poulin’s event-focused text analytics to create a model to predict those with suicidal or other harmful tendencies.
Dubbed the Durkheim Project (after French sociologist Emile Durkheim known for his 19th century study of suicide data) the researchers ultimately hope to use opt-in data from veterans’ social media and mobile content to create a real-time predictive analytics tool for suicide risk. While the team behind the project is optimistic about its abilities to make predictions with 65 percent accuracy, the challenge at this stage is about gaining the cooperation of veterans to join the effort to gain insights into their well-being.