@inproceedings{wang-etal-2024-wonder,
    title = "Wonder at Chemotimelines 2024: {M}ed{T}imeline: An End-to-End {NLP} System for Timeline Extraction from Clinical Narratives",
    author = "Wang, Liwei  and
      Lu, Qiuhao  and
      Li, Rui  and
      Fu, Sunyang  and
      Liu, Hongfang",
    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/Ingest-2025-COMPUTEL/2024.clinicalnlp-1.48/",
    doi = "10.18653/v1/2024.clinicalnlp-1.48",
    pages = "483--487",
    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."
}