introduce bias.Hollingshead believes that OurDataHelps can create a gold standard by offering scientists a comprehensive and tested dataset to inform their models and algorithms. With that kind of accuracy, Hollingshead envisions a world in which mental well-being becomes part of the quantified self; someone who has suicidal feelings, or has attempted suicide in the past, could use software or an app to run an algorithm on their social media feeds for imminent risk that they might not even sense. "Language is such a good signal of what's going on in your brain," she says. "Even if you tried to change it so you seemed happy, the algorithms can still pick up on the underlying currents of what's going on." The path to suicide is not straight The Department of Defense recently released three studies that show just how promising statistical analysis can be when applied unconventionally to the problem of predicting suicide. The research, conducted by Craig Bryan, a prominent suicide-prevention researcher, executive director of the National Center for Veterans Studies at The University of Utah and co-investigator on the OurDataHelps initiative, used a mathematical concept known as dynamic systems modeling. Instead of looking at the classic single data point when a person died by suicide the researchers were able to incorporate real-time mood and language prior to their subjects' deaths by scouring publicly available social media posts. SEE ALSO: 7 initial ways you can be a better mental health ally and advocateThe work began in 2013 when Bryan and his co-authors set out to understand whether a service member's social media data held clues about their suicide risk. In particular, the researchers analyzed Facebook posts from a sample of 700 service members who died by suicide and 700 service members who died from other causes during a two-year-period. They not only confirmed their original hypothesis, but retrospectively predicted a person's suicide and discovered patterns that challenge our assumptions about how suicide unfolds. Bryan's research reveals that the pathway to suicide is not linear, contrary to the popular belief that such thoughts and behavior become an unyielding downward spiral. Instead, his study found important fluctuations in each service member's path to suicide. Those who ended their lives were more likely to post about a stressful life event and then share negative emotions and beliefs; the sequence of those posts was reversed for those in the second group. Prior to three months before the subjects' deaths by suicide, they tended to post about physical symptoms, emotions and thoughts in close proximity. At the three-month mark, that trend disappeared. Then, in the month before dying, they often posted about "maladaptive behavior," like social withdrawal and alcohol use, while updates describing their worldview and self-perceptions became more stable. "What we did in the beginning was look at it like a straight line," Bryan says. "But thinking in ups and downs, like a roller coaster, with much more complex interactions that significantly improves our ability to detect suicidal individuals, and it provides a possible way to estimate, when are they going to die?"Skepticism and hope Sheila