Abstract
Background: Halbert L. Dunn's concept of wellness is a multi-dimensional aspect encompassing social and mental well-being. Neglecting these dimensions over time can have a negative impact on an individual's mental health. The manual efforts employed in in-person therapy sessions reveal that underlying factors of mental disturbance if triggered, may lead to severe mental health disorders. Objective: In our research, we introduce a fine-grained approach focused on identifying indicators of wellness dimensions and mark their presence in self-narrated human-writings on Reddit social media platform. Design and Method: We present the MULTIWD dataset, a curated collection comprising 3281 instances, as a specifically designed and annotated dataset that facilitates the identification of multiple wellness dimensions in Reddit posts. In our study, we introduce the task of identifying wellness dimensions and utilize state-of-the-art classifiers to solve this multi-label classification task. Results: Our findings highlights the best and comparative performance of fine-tuned large language models with fine-tuned BERT model. As such, we set BERT as a baseline model to tag wellness dimensions in a user-penned text with F1 score of 76.69. Conclusion: Our findings underscore the need of trustworthy and domain-specific knowledge infusion to develop more comprehensive and contextually-aware AI models for tagging and extracting wellness dimensions.
Original language | English (US) |
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Article number | 104586 |
Journal | Journal of Biomedical Informatics |
Volume | 150 |
DOIs | |
State | Published - Feb 2024 |
Keywords
- Dataset
- Mental health
- Multi-label classification
- Wellness dimensions
ASJC Scopus subject areas
- Health Informatics
- Computer Science Applications