Detecting suicide risk among U.S. servicemembers and veterans: a deep learning approach using social media data
Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.
Authors
- Daniel M. Low (ORCID: https://orcid.org/0000-0002-8866-8667)
- Richard Kuzma
- Kelly L. Zuromski (ORCID: https://orcid.org/0000-0002-4798-898X)
- Erik K. Kastman (ORCID: https://orcid.org/0000-0001-7221-9042)
- Satrajit Ghosh (ORCID: https://orcid.org/0000-0002-5312-6729)
- Jonathan I. Epstein (ORCID: https://orcid.org/0000-0003-1839-6472)
- Matthew K. Nock (ORCID: https://orcid.org/0000-0001-6508-1145)
- David Gowel
- Carlos Madden
- Noah C. Jones
- Daniel Kessler (ORCID: https://orcid.org/0000-0002-5054-7982)
- Liutong Zhou
Institutions
- Amazon (United States) (US)
- Harvard University (US)
- McGovern Institute for Brain Research (US)
- Harvard University Press (US)
- Bioscience Research (US)
- Bridgepoint (United Kingdom) (GB)
- Franciscan Hospital for Children (US)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Psychological Medicine
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1017/s0033291724001557
- Citations
- 7
- Primary Topic
- Mental Health via Writing
- Type
- article
- Field-Weighted Citation Impact
- 4.50
Funders
- Eli Lilly and Company
- Small Business Innovation Research
- U.S. Air Force
- National Institute of Mental Health
- National Institute on Deafness and Other Communication Disorders
- Air Force Research Laboratory