@inproceedings{zecevic-etal-2026-uncertainty,
    title = "Uncertainty-Aware Multi-Label Routing of Clinical Text to Surveillance Pathways",
    author = "Zecevic, Agathe  and
      Zeki, Sebastian  and
      Roberts, Angus",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.16/",
    pages = "181--190",
    ISBN = "979-8-89176-434-7",
    abstract = "Clinical decision support systems that operate across multiple downstream care pathways must first determine which pathway or pathways are relevant for a given patient. We study this routing problem in gastrointestinal surveillance, where paired endoscopy and histopathology text reports may indicate multiple concurrent conditions and therefore require multi-label routing. In this context, standard hard-label evaluation can be insufficient: a model may achieve reasonable overall performance while still excluding clinically important pathways when uncertain. We formulate gastrointestinal report routing as a multi-label uncertainty-aware classification task over six pathway labels and compare lightweight lexical baselines, frozen embedding models and a fine-tuned transformer baseline under two complementary uncertainty mechanisms: threshold-based abstention and set-valued conformal prediction. Using 1,773 paired reports from a single NHS trust with disjoint train, calibration and test splits, we evaluate both hard-routing performance and the downstream review burden introduced by uncertainty-aware prediction. The fine-tuned ClinicalBERT model achieved the strongest overall performance (0.811 subset accuracy, 0.861 macro-F1) and the lowest AURC of 0.084 under min-margin abstention. Threshold-based abstention consistently reduced exact-match routing error on accepted reports. For conformal routing at ?=0.10, Mondrian calibration achieved high mean positive-label recall coverage across learned baselines (0.883-0.917). The fine-tuned model achieved 0.891 mean recall coverage with a mean prediction set size of 1.70, 0.642 candidate-label precision and 0.61 false-positive labels per report. Compared with a recall-tuned threshold baseline at similar recall, Mondrian CP produced smaller candidate sets, higher candidate-label precision and fewer false-positive pathway suggestions. These results show that uncertainty-aware evaluation exposes clinically important failure modes missed by aggregate metrics. They also show that high-recall routing is not cost-free: set-valued prediction can reduce missed-pathway risk but must be interpreted as candidate generation for downstream review rather than automated pathway selection."
}