@inproceedings{haddadan-etal-2024-lailab,
    title = "{LAIL}ab at Chemotimelines 2024: Finetuning sequence-to-sequence language models for temporal relation extraction towards cancer patient undergoing chemotherapy treatment",
    author = "Haddadan, Shohreh  and
      Le, Tuan-Dung  and
      Duong, Thanh  and
      Thieu, Thanh",
    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/jlcl-multiple-ingestion/2024.clinicalnlp-1.37/",
    doi = "10.18653/v1/2024.clinicalnlp-1.37",
    pages = "382--393",
    abstract = "In this paper, we report our effort to tackle the challenge of extracting chemotimelines from EHR notes across a dataset of three cancer types. We focus on the two subtasks: 1) detection and classification of temporal relations given the annotated chemotherapy events and time expressions and 2) directly extracting patient chemotherapy timelines from EHR notes. We address both subtasks using Large Language Models. Our best-performing methods in both subtasks use Flan-T5, an instruction-tuned language model. Our proposed system achieves the highest average score in both subtasks. Our results underscore the effectiveness of finetuning general-domain large language models in domain-specific and unseen tasks."
}