Abstract
Extracting timeline information from clinical narratives is critical for cancer research and practice using electronic health records (EHRs). In this study, we apply MedTimeline, our end-to-end hybrid NLP system combining large language model, deep learning with knowledge engineering, to the ChemoTimeLine challenge subtasks. Our experiment results in 0.83, 0.90, 0.84, and 0.53, 0.63, 0.39, respectively, for subtask1 and subtask2 in breast, melanoma and ovarian cancer.- Anthology ID:
- 2024.clinicalnlp-1.48
- 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:
- 483–487
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.48
- DOI:
- Cite (ACL):
- Liwei Wang, Qiuhao Lu, Rui Li, Sunyang Fu, and Hongfang Liu. 2024. Wonder at Chemotimelines 2024: MedTimeline: An End-to-End NLP System for Timeline Extraction from Clinical Narratives. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 483–487, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Wonder at Chemotimelines 2024: MedTimeline: An End-to-End NLP System for Timeline Extraction from Clinical Narratives (Wang et al., ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.48.pdf