Abstract
The extraction of chemotherapy treatment timelines from clinical narratives poses significant challenges due to the complexity of medical language and patient-specific treatment regimens. This paper describes the NYULangone team’s approach to Subtask 2 of the Chemotimelines 2024 shared task, focusing on leveraging a locally hosted Large Language Model (LLM), Mixtral 8x7B (Mistral AI, France), to interpret and extract relevant events from clinical notes without relying on domain-specific training data. Despite facing challenges due to the task’s complexity and the current capacity of open-source AI, our methodology highlights the future potential of local foundational LLMs in specialized domains like biomedical data processing.- Anthology ID:
- 2024.clinicalnlp-1.42
- 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:
- 428–430
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.42
- DOI:
- Cite (ACL):
- Jeff Zhang, Yin Aphinyanaphongs, and Anthony Cardillo. 2024. NYULangone at Chemotimelines 2024: Utilizing Open-Weights Large Language Models for Chemotherapy Event Extraction. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 428–430, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- NYULangone at Chemotimelines 2024: Utilizing Open-Weights Large Language Models for Chemotherapy Event Extraction (Zhang et al., ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.42.pdf