@inproceedings{chen-etal-2026-factrial,
    title = "{FACT}rial: Factorized Clinical Contrastive Training for Scalable Patient-Trial Retrieval",
    author = "Chen, Xuanren  and
      Tao, Chongyang  and
      Shen, Tao  and
      Ma, Shuai",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.874/",
    pages = "19127--19145",
    ISBN = "979-8-89176-390-6",
    abstract = "Patient{--}trial retrieval is a challenging problem that requires nuanced clinical reasoning beyond surface-level semantic similarity. However, scarce and costly relevance annotations force existing approaches to rely on very limited supervision or zero-shot transfer, reducing the task to generic semantic matching and failing to capture multi-factor eligibility reasoning. To this end, we propose FACTrial, a factorized contrastive training framework that leverages LLMs to synthesize diagnosis-aware supervision for scalable patient{--}trial retrieval. FACTrial decomposes each patient note into a primary diagnosis and a set of concomitant, eligibility-triggering conditions, and constructs complementary contrastive signals through structured trial augmentation. Specifically, we generate primary-target and concomitant-target positives, together with clinically confusable near-miss negatives, to enforce diagnostic specificity under contrastive learning. Two specialized bi-encoder experts are trained to balance primary-diagnosis prioritization and concomitant-driven recall, and fused into a single deployable retriever. Experiments on three public benchmarks demonstrate that FACTrial achieves state-of-the-art performance, improving both top-ranked quality and high-recall coverage."
}