@inproceedings{harne-etal-2024-case,
title = "{CASE}: Efficient Curricular Data Pre-training for Building Assistive Psychology Expert Models",
author = "Harne, Sarthak and
Choudhury, Monjoy Narayan and
Rao, Madhav and
Srikanth, T K and
Mehrotra, Seema and
Vashisht, Apoorva and
Basu, Aarushi and
Sodhi, Manjit Singh",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.925/",
doi = "10.18653/v1/2024.findings-emnlp.925",
pages = "15769--15778",
abstract = "The limited availability of psychologists necessitates efficient identification of individuals requiring urgent mental healthcare. This study explores the use of Natural Language Processing (NLP) pipelines to analyze text data from online mental health forums used for consultations. By analyzing forum posts, these pipelines can flag users who may require immediate professional attention. A crucial challenge in this domain is data privacy and scarcity. To address this, we propose utilizing readily available curricular texts used in institutes specializing in mental health for pre-training the NLP pipelines. This helps us mimic the training process of a psychologist. Our work presents CASE-BERT that flags potential mental health disorders based on forum text. CASE-BERT demonstrates superior performance compared to existing methods, achieving an f1 score of 0.91 for Depression and 0.88 for Anxiety, two of the most commonly reported mental health disorders. Our code and data are publicly available."
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<abstract>The limited availability of psychologists necessitates efficient identification of individuals requiring urgent mental healthcare. This study explores the use of Natural Language Processing (NLP) pipelines to analyze text data from online mental health forums used for consultations. By analyzing forum posts, these pipelines can flag users who may require immediate professional attention. A crucial challenge in this domain is data privacy and scarcity. To address this, we propose utilizing readily available curricular texts used in institutes specializing in mental health for pre-training the NLP pipelines. This helps us mimic the training process of a psychologist. Our work presents CASE-BERT that flags potential mental health disorders based on forum text. CASE-BERT demonstrates superior performance compared to existing methods, achieving an f1 score of 0.91 for Depression and 0.88 for Anxiety, two of the most commonly reported mental health disorders. Our code and data are publicly available.</abstract>
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%0 Conference Proceedings
%T CASE: Efficient Curricular Data Pre-training for Building Assistive Psychology Expert Models
%A Harne, Sarthak
%A Choudhury, Monjoy Narayan
%A Rao, Madhav
%A Srikanth, T. K.
%A Mehrotra, Seema
%A Vashisht, Apoorva
%A Basu, Aarushi
%A Sodhi, Manjit Singh
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F harne-etal-2024-case
%X The limited availability of psychologists necessitates efficient identification of individuals requiring urgent mental healthcare. This study explores the use of Natural Language Processing (NLP) pipelines to analyze text data from online mental health forums used for consultations. By analyzing forum posts, these pipelines can flag users who may require immediate professional attention. A crucial challenge in this domain is data privacy and scarcity. To address this, we propose utilizing readily available curricular texts used in institutes specializing in mental health for pre-training the NLP pipelines. This helps us mimic the training process of a psychologist. Our work presents CASE-BERT that flags potential mental health disorders based on forum text. CASE-BERT demonstrates superior performance compared to existing methods, achieving an f1 score of 0.91 for Depression and 0.88 for Anxiety, two of the most commonly reported mental health disorders. Our code and data are publicly available.
%R 10.18653/v1/2024.findings-emnlp.925
%U https://aclanthology.org/2024.findings-emnlp.925/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.925
%P 15769-15778
Markdown (Informal)
[CASE: Efficient Curricular Data Pre-training for Building Assistive Psychology Expert Models](https://aclanthology.org/2024.findings-emnlp.925/) (Harne et al., Findings 2024)
ACL
- Sarthak Harne, Monjoy Narayan Choudhury, Madhav Rao, T K Srikanth, Seema Mehrotra, Apoorva Vashisht, Aarushi Basu, and Manjit Singh Sodhi. 2024. CASE: Efficient Curricular Data Pre-training for Building Assistive Psychology Expert Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15769–15778, Miami, Florida, USA. Association for Computational Linguistics.