@inproceedings{zhang-etal-2024-nyulangone,
    title = "{NYUL}angone at Chemotimelines 2024: Utilizing Open-Weights Large Language Models for Chemotherapy Event Extraction",
    author = "Zhang, Jeff  and
      Aphinyanaphongs, Yin  and
      Cardillo, Anthony",
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Bitterman, Danielle",
    booktitle = "Proceedings of the 6th Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.clinicalnlp-1.42/",
    doi = "10.18653/v1/2024.clinicalnlp-1.42",
    pages = "428--430",
    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."
}