@inproceedings{macavaney-etal-2021-community,
title = "Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the {CLP}sych 2021 Shared Task",
author = "MacAvaney, Sean and
Mittu, Anjali and
Coppersmith, Glen and
Leintz, Jeff and
Resnik, Philip",
editor = "Goharian, Nazli and
Resnik, Philip and
Yates, Andrew and
Ireland, Molly and
Niederhoffer, Kate and
Resnik, Rebecca",
booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.clpsych-1.7/",
doi = "10.18653/v1/2021.clpsych-1.7",
pages = "70--80",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task
%A MacAvaney, Sean
%A Mittu, Anjali
%A Coppersmith, Glen
%A Leintz, Jeff
%A Resnik, Philip
%Y Goharian, Nazli
%Y Resnik, Philip
%Y Yates, Andrew
%Y Ireland, Molly
%Y Niederhoffer, Kate
%Y Resnik, Rebecca
%S Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F macavaney-etal-2021-community
%X 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.
%R 10.18653/v1/2021.clpsych-1.7
%U https://aclanthology.org/2021.clpsych-1.7/
%U https://doi.org/10.18653/v1/2021.clpsych-1.7
%P 70-80
Markdown (Informal)
[Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task](https://aclanthology.org/2021.clpsych-1.7/) (MacAvaney et al., CLPsych 2021)
ACL