Context-aware Medication Event Extraction from Unstructured Text
Noushin Salek Faramarzi, Meet Patel, Sai Harika Bandarupally, Ritwik Banerjee
Abstract
Accurately capturing medication history is crucial in delivering high-quality medical care. The extraction of medication events from unstructured clinical notes, however, is challenging because the information is presented in complex narratives. We address this challenge by leveraging the newly released Contextualized Medication Event Dataset (CMED) as part of our participation in the 2022 National NLP Clinical Challenges (n2c2) shared task. Our study evaluates the performance of various pretrained language models in this task. Further, we find that data augmentation coupled with domain-specific training provides notable improvements. With experiments, we also underscore the importance of careful data preprocessing in medical event detection.- Anthology ID:
- 2023.clinicalnlp-1.11
- Volume:
- Proceedings of the 5th Clinical Natural Language Processing Workshop
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Anna Rumshisky
- Venue:
- ClinicalNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 86–95
- Language:
- URL:
- https://aclanthology.org/2023.clinicalnlp-1.11
- DOI:
- 10.18653/v1/2023.clinicalnlp-1.11
- Cite (ACL):
- Noushin Salek Faramarzi, Meet Patel, Sai Harika Bandarupally, and Ritwik Banerjee. 2023. Context-aware Medication Event Extraction from Unstructured Text. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 86–95, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Context-aware Medication Event Extraction from Unstructured Text (Salek Faramarzi et al., ClinicalNLP 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.clinicalnlp-1.11.pdf