In late 2019, a group of researchers discovered that not only does media coverage impact national suicide rates, but that coverage that isn’t aligned to the guidelines established in 2001 for reporting on suicide risks increasing national suicide rates by up to 13% (https://www.bmj.com/content/368/bmj.m575). In response, a team of nonprofit and for-profit organizations came together (including the researcher who created the original reporting guidelines, which have been endorsed by the WHO and CDC) to develop an ML-based UI for journalists (similar to Grammarly) to increase the adoption rate of these standards. In this session, we (if approved, a suicide prevention researcher will co-present with me) will demonstrate how this ML will save hundreds of thousands of lives as well as the best practices we’re using to maximize its technical efficacy and social impact. Note that this session will include a live demo.

Session Overview

  • 1

    Data Science for Suicide Prevention

    • Abstract & Bio

    • Data Science for Suicide Prevention


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