Devashru Patel, Steven A Sumner, Daniel Bowen, Marissa Zwald, Ellen Yard, Jing Wang, Royal Law, Kristin Holland, Theresa Nguyen, Gary Mower, Yushiuan Chen, Jenna Iberg Johnson, Megan Jespersen, Elizabeth Mytty, Jennifer M Lee, Michael Bauer, Eric Caine, Munmun De Choudhury
Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states...
January 16, 2024: Npj Ment Health Res