Enabling faster intervention of military community suicides
The U.S. Department of Veterans Affairs estimates that approximately 17 military veterans die by suicide each day. That rate is 50% higher than that of non-veteran adults. Harvard University’s Nock Lab, Amazon Web Services (AWS), and RallyPoint—a social media platform designed for the broader U.S. military community—have been working together to tackle this challenge. Now AWS and RallyPoint have built a machine learning model that can quickly analyze public posts on the RallyPoint platform and help determine whether there is an indication of self-harm.
Since its founding in 2012, RallyPoint has provided an online user experience focused on military service members, veterans, families, caregivers, and survivors to help them lead more successful and fulfilling lives.
In order to speed the discovery of these important posts, RallyPoint began working with mental health experts at Harvard University and the Amazon Machine Learning (ML) Solutions Lab, which pairs customers with AWS's ML experts to help identify and build ML solutions to address a critical business need.
The Amazon ML Solutions Lab collaborated with RallyPoint to build a machine learning model using Amazon SageMaker trained with anonymized public posts. Then, mental health experts at Harvard helped refine the model by annotating additional posts using Amazon SageMaker Ground Truth in order to continuously improve the accuracy of the predictions made by the model.
The machine learning model is helping to quickly surface sensitive public posts to RallyPoint and Harvard teams, while reducing the amount of manual review needed to enable a potentially life-saving intervention.
Sep 03, 2020 at 20:06