@inproceedings{aich-parde-2022-really,
title = "Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses",
author = "Aich, Ankit and
Parde, Natalie",
editor = "Zirikly, Ayah and
Atzil-Slonim, Dana and
Liakata, Maria and
Bedrick, Steven and
Desmet, Bart and
Ireland, Molly and
Lee, Andrew and
MacAvaney, Sean and
Purver, Matthew and
Resnik, Rebecca and
Yates, Andrew",
booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
month = jul,
year = "2022",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.clpsych-1.8",
doi = "10.18653/v1/2022.clpsych-1.8",
pages = "89--104",
abstract = "Evidence has demonstrated the presence of similarities in language use across people with various mental health conditions. In this work, we investigate these correlations both in terms of literature and as a data analysis problem. We also introduce a novel state-of-the-art transfer learning-based approach that learns from linguistic feature spaces of previous conditions and predicts unknown ones. Our model achieves strong performance, with F1 scores of 0.75, 0.80, and 0.76 at detecting depression, stress, and suicidal ideation in a first-of-its-kind transfer task and offering promising evidence that language models can harness learned patterns from known mental health conditions to aid in their prediction of others that may lie latent.",
}
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%0 Conference Proceedings
%T Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses
%A Aich, Ankit
%A Parde, Natalie
%Y Zirikly, Ayah
%Y Atzil-Slonim, Dana
%Y Liakata, Maria
%Y Bedrick, Steven
%Y Desmet, Bart
%Y Ireland, Molly
%Y Lee, Andrew
%Y MacAvaney, Sean
%Y Purver, Matthew
%Y Resnik, Rebecca
%Y Yates, Andrew
%S Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F aich-parde-2022-really
%X Evidence has demonstrated the presence of similarities in language use across people with various mental health conditions. In this work, we investigate these correlations both in terms of literature and as a data analysis problem. We also introduce a novel state-of-the-art transfer learning-based approach that learns from linguistic feature spaces of previous conditions and predicts unknown ones. Our model achieves strong performance, with F1 scores of 0.75, 0.80, and 0.76 at detecting depression, stress, and suicidal ideation in a first-of-its-kind transfer task and offering promising evidence that language models can harness learned patterns from known mental health conditions to aid in their prediction of others that may lie latent.
%R 10.18653/v1/2022.clpsych-1.8
%U https://aclanthology.org/2022.clpsych-1.8
%U https://doi.org/10.18653/v1/2022.clpsych-1.8
%P 89-104
Markdown (Informal)
[Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses](https://aclanthology.org/2022.clpsych-1.8) (Aich & Parde, CLPsych 2022)
ACL