Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task

Sean MacAvaney, Anjali Mittu, Glen Coppersmith, Jeff Leintz, Philip Resnik


Abstract
Progress on NLP for mental health — indeed, for healthcare in general — is hampered by obstacles to shared, community-level access to relevant data. We report on what is, to our knowledge, the first attempt to address this problem in mental health by conducting a shared task using sensitive data in a secure data enclave. Participating teams received access to Twitter posts donated for research, including data from users with and without suicide attempts, and did all work with the dataset entirely within a secure computational environment. We discuss the task, team results, and lessons learned to set the stage for future tasks on sensitive or confidential data.
Anthology ID:
2021.clpsych-1.7
Volume:
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
Month:
June
Year:
2021
Address:
Online
Venues:
CLPsych | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
70–80
Language:
URL:
https://aclanthology.org/2021.clpsych-1.7
DOI:
10.18653/v1/2021.clpsych-1.7
Bibkey:
Cite (ACL):
Sean MacAvaney, Anjali Mittu, Glen Coppersmith, Jeff Leintz, and Philip Resnik. 2021. Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 70–80, Online. Association for Computational Linguistics.
Cite (Informal):
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task (MacAvaney et al., CLPsych 2021)
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PDF:
https://aclanthology.org/2021.clpsych-1.7.pdf