@inproceedings{jain-etal-2025-team,
    title = "{TEAM} {UAB} at Chemotherapy Timelines 2025: Integrating Encoders and Large Language Models for Chemotherapy Timelines Generation",
    author = "Jain, Vijay Raj  and
      Coffee, Chris  and
      He, Kaiwen  and
      Cron, Remy  and
      Cochran, Micah D.  and
      Mansilla-Gonzalez, Luis  and
      Nadimpalli, Akhil  and
      Murad, Danish  and
      Osborne, John D",
    editor = "Ben Abacha, Asma  and
      Bethard, Steven  and
      Bitterman, Danielle  and
      Naumann, Tristan  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 7th Clinical Natural Language Processing Workshop",
    month = oct,
    year = "2025",
    address = "Virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-alta/2025.clinicalnlp-1.5/",
    pages = "30--39",
    abstract = "Reconstructing the timeline of Systemic Anticancer Therapy (SACT) or ``chemotherapy'' from heterogeneous Electronic Health Record(EHR) notes is a challenging task. Rapid developments in Large Language Models (LLMs), including a range of architectural improvements and post-training refinements since the 2024 Chemotherapy Timelines Task could make this task more tractable. We evaluated the performance of 4 recently released LLMs (GPT-4.1-mini, Phi4 and 2 Qwen3 models) on this task. Our results indicate that even witha variety of prompt optimization and synthetic data training, more work is still needed to see a useful application of this work."
}