data and research that lead to new treatments for mental health conditions, Coppersmith wants to ultimately save lives by understanding how a person behaves in the days, weeks and months before becoming suicidal, and intervene before they have a chance to attempt suicide. Social media and mobile data provide a new opportunity to make that a reality. April Foreman, suicide prevention coordinator at the Southeast Louisiana Veterans Health Care System in Baton Rouge, Louisiana, has been involved with OurDataHelps from its inception as an adviser. Describing the project to suicide survivors, she says, prompts a heartbreaking response. "The look on their faces it's that moment [when a person recognizes] that out of great tragedy you can salvage something," she says. "There's some precious thing left that makes a difference; a small good that can be pulled out of this horrible tragedy." Unanswered questions That tragedy is often traced back to risk factors like a family history of suicide, alcohol or drug addiction, and feelings of hopelessness. But patients with similar life stories routinely have different outcomes, and researchers have long struggled to understand the life-saving difference.Studies conducted in the past few decades have generated important findings, but experts in the field say that prevention information is frequently vague, outdated or based on ineffective research methods. Scientists studying suicide may follow the same variables in people's lives over a long period of time. They've even analyzed personal journals, tallying how many times certain words appear in order to understand people's minds in the fog of depression and suicidal thinking. What they haven't been able to do, however, is perform that analysis on a large scale and quickly gather information about people's everyday experiences.The beauty of the algorithms behind OurDataHelps, Foreman says, is that they can do what she can't: count. Foreman regularly looks at the medical records of 80 to 120 high-risk veterans, most of whom won't die by suicide. With training and expertise, she knows how to identify patients with serious warning signs, but she also understands the limitations of the human brain. It can't track and analyze, for instance, speech patterns in real time, show a statistically significant pattern and link that with patients more likely to attempt suicide. Building the right systemsKristy Hollingshead, who has co-authored studies with Coppersmith that detect and analyze post-traumatic stress, depression and ADHD on social media, says online communication contains a "very strong, consistent, dependable signal" about a person's mental health. Machine learning technology allows Hollingshead, a research scientist at the Florida Institute for Human & Machine Cognition, to capture and analyze thousands of public posts from at least several hundred users. In aggregate, those posts can easily become hundreds of thousands of lines of text, which she couldn't possibly scroll through and code manually. Despite the efficiency of outsourcing these tasks to a computer, the systems Hollingshead and her colleagues are building aren't as accurate as they'd like them to be. They're making educated guesses about their data estimating, for example, user gender and age. Using samples from small groups of users can also