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    <abstract>Social media has become a popular platform where people share information about personal healthcare conditions, diagnostic histories, and medical plans. Analyzing posts on social media depicting such realistic information can help improve quality and clinical decision-making; however, the lack of structured resources in this genre limits us to build robust NLP models for meaningful analysis. This paper presents a new corpus annotating relations among many types of conditions, treatments, and their attributes illustrated in social media posts by patients and caregivers. For experiments, a transformer encoder is pretrained on 1M raw posts and used to train several document-level relation extraction models using our corpus. Our best-performing model achieves the F1 scores of 70.9 and 51.7 for Entity Recognition and Relation Extraction, respectively. These results are encouraging as it is the first neural model extracting complex relations of this kind on social media data.</abstract>
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%0 Conference Proceedings
%T Condition-Treatment Relation Extraction on Disease-related Social Media Data
%A Tu, Sichang
%A Doogan, Stephen
%A Choi, Jinho D.
%Y Lavelli, Alberto
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F tu-etal-2022-condition
%X Social media has become a popular platform where people share information about personal healthcare conditions, diagnostic histories, and medical plans. Analyzing posts on social media depicting such realistic information can help improve quality and clinical decision-making; however, the lack of structured resources in this genre limits us to build robust NLP models for meaningful analysis. This paper presents a new corpus annotating relations among many types of conditions, treatments, and their attributes illustrated in social media posts by patients and caregivers. For experiments, a transformer encoder is pretrained on 1M raw posts and used to train several document-level relation extraction models using our corpus. Our best-performing model achieves the F1 scores of 70.9 and 51.7 for Entity Recognition and Relation Extraction, respectively. These results are encouraging as it is the first neural model extracting complex relations of this kind on social media data.
%R 10.18653/v1/2022.louhi-1.24
%U https://aclanthology.org/2022.louhi-1.24
%U https://doi.org/10.18653/v1/2022.louhi-1.24
%P 218-228

Markdown (Informal)

[Condition-Treatment Relation Extraction on Disease-related Social Media Data](https://aclanthology.org/2022.louhi-1.24) (Tu et al., Louhi 2022)

• Condition-Treatment Relation Extraction on Disease-related Social Media Data (Tu et al., Louhi 2022)

ACL

• Sichang Tu, Stephen Doogan, and Jinho D. Choi. 2022. Condition-Treatment Relation Extraction on Disease-related Social Media Data. In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI), pages 218–228, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.