Abstract
This paper presents our participation in the Chemotimelines 2024 subtask2, focusing on the development of an end-to-end system for chemotherapy timeline extraction. We initially adopt a basic framework from subtask2, utilizing Apache cTAKES for entity recognition and a BERT-based model for classifying the temporal relationship between chemotherapy events and associated times. Subsequently, we enhance this pipeline through two key directions: first, by expanding the exploration of the system, achieved by extending the search dictionary of cTAKES with the UMLS database; second, by reducing false positives through preprocessing of clinical notes and implementing filters to reduce the potential errors from the BERT-based model. To validate the effectiveness of our framework, we conduct extensive experiments using clinical notes from breast, ovarian, and melanoma cancer cases. Our results demonstrate improvements over the previous approach.- Anthology ID:
- 2024.clinicalnlp-1.40
- Volume:
- Proceedings of the 6th Clinical Natural Language Processing Workshop
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
- Venues:
- ClinicalNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 417–421
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.40
- DOI:
- 10.18653/v1/2024.clinicalnlp-1.40
- Cite (ACL):
- Yukun Tan, Merve Dede, and Ken Chen. 2024. KCLab at Chemotimelines 2024: End-to-end system for chemotherapy timeline extraction – Subtask2. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 417–421, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- KCLab at Chemotimelines 2024: End-to-end system for chemotherapy timeline extraction – Subtask2 (Tan et al., ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.clinicalnlp-1.40.pdf