@inproceedings{zhang-etal-2025-improving-barretts,
    title = "Improving Barrett{'}s Oesophagus Surveillance Scheduling with Large Language Models: A Structured Extraction Approach",
    author = "Zhang, Xinyue  and
      Zecevic, Agathe  and
      Zeki, Sebastian  and
      Roberts, Angus",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Tsujii, Junichi",
    booktitle = "ACL 2025",
    month = aug,
    year = "2025",
    address = "Viena, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-1.16/",
    pages = "176--189",
    ISBN = "979-8-89176-275-6",
    abstract = "Gastroenterology (GI) cancer surveillance scheduling relies on extracting structured data from unstructured clinical texts, such as endoscopy and pathology reports. Traditional Natural Language Processing (NLP) models have been employed for this task, but recent advancements in Large Language Models (LLMs) present a new opportunity for automation without requiring extensive labeled datasets. In this study, we propose an LLM-based entity extraction and rule-based decision support framework for Barrett{'}s Oesophagus (BO) surveillance timing prediction. Our approach processes endoscopy and pathology reports to extract clinically relevant information and structures it into a standardised format, which is then used to determine appropriate surveillance intervals. We evaluate multiple state-of-the-art LLMs on real-world clinical datasets from two hospitals, assessing their performance in accuracy and running time cost. The results demonstrate that LLMs, particularly Phi-4 and (DeepSeek distilled) Qwen-2.5, can effectively automate the extraction of BO surveillance-related information with high accuracy, while Phi-4 is also efficient during inference. We also compared the trade-offs between LLMs and fine-tuned non-LLMs. Our findings indicate that LLM extraction based methods can support clinical decision-making by providing justifications from report extractions, reducing manual workload, and improving guideline adherence in BO surveillance scheduling."
}