@inproceedings{deepak-etal-2026-ltrc,
    title = "{LTRC}-Medicom at {MEDIQA}-{SYNUR} 2026: Schema-Guided Clinical Information Extraction with Hybrid Clustering-{SFT}-Verification",
    author = "Deepak, Pasumarthy  and
      Marimuthu, Sushvin  and
      Krishnamurthy, Parameswari",
    editor = "Ben Abacha, Asma  and
      Bethard, Steven  and
      Bitterman, Danielle  and
      Naumann, Tristan  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical {NLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/cawl-year/2026.clinicalnlp-1.25/",
    doi = "10.63317/4oou5ss2efr6",
    pages = "228--234",
    abstract = "Extracting structured clinical data from unstructured patient transcripts is challenging due to large target schemas and inherent linguistic ambiguity. We address the extraction of 193 heterogeneous clinical attributes from nursing notes and clinician{--}patient dialogues, and demonstrate that zero-shot large language models (LLMs) are ineffective in this setting, achieving an F1 score below 0.15 due to context window saturation and hallucination. We propose a four-stage framework that combines semantic schema clustering, role-based chain-of-thought prompting, supervised fine-tuning of Llama-3.1-8B, and transcript-verified post-processing. Our approach achieves an F1 score of 0.66, representing a 4.4x improvement over the baseline, by balancing high recall from generative models with high precision from verification. These results highlight the effectiveness of hybrid pipelines for high-stakes clinical information extraction."
}