Normalization of Long-tail Adverse Drug Reactions in Social Media

Emmanouil Manousogiannis, Sepideh Mesbah, Alessandro Bozzon, Robert-Jan Sips, Zoltan Szlanik, Selene Baez


Abstract
The automatic mapping of Adverse Drug Reaction (ADR) reports from user-generated content to concepts in a controlled medical vocabulary provides valuable insights for monitoring public health. While state-of-the-art deep learning-based sequence classification techniques achieve impressive performance for medical concepts with large amounts of training data, they show their limit with long-tail concepts that have a low number of training samples. The above hinders their adaptability to the changes of layman’s terminology and the constant emergence of new informal medical terms. Our objective in this paper is to tackle the problem of normalizing long-tail ADR mentions in user-generated content. In this paper, we exploit the implicit semantics of rare ADRs for which we have few training samples, in order to detect the most similar class for the given ADR. The evaluation results demonstrate that our proposed approach addresses the limitations of the existing techniques when the amount of training data is limited.
Anthology ID:
2020.louhi-1.6
Volume:
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Louhi
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
49–58
Language:
URL:
https://aclanthology.org/2020.louhi-1.6
DOI:
10.18653/v1/2020.louhi-1.6
Bibkey:
Cite (ACL):
Emmanouil Manousogiannis, Sepideh Mesbah, Alessandro Bozzon, Robert-Jan Sips, Zoltan Szlanik, and Selene Baez. 2020. Normalization of Long-tail Adverse Drug Reactions in Social Media. In Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, pages 49–58, Online. Association for Computational Linguistics.
Cite (Informal):
Normalization of Long-tail Adverse Drug Reactions in Social Media (Manousogiannis et al., Louhi 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.louhi-1.6.pdf
Video:
 https://slideslive.com/38940039
Data
MultiNLISNLI