Uncertainty-Aware Multi-Label Routing of Clinical Text to Surveillance Pathways

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing · Depth: Expert, quick

Summary

A study on uncertainty-aware multi-label routing of clinical text to surveillance pathways addresses the challenge of assigning gastrointestinal endoscopy and histopathology reports to multiple care pathways. Researchers formulated this as a multi-label classification task across six pathway labels, comparing lexical baselines, frozen embedding models, and a fine-tuned transformer model. Using 1,773 paired reports from an NHS trust, they evaluated performance with threshold-based abstention and set-valued conformal prediction. The fine-tuned ClinicalBERT model achieved superior performance (0.811 subset accuracy, 0.861 macro-F1) and the lowest AURC of 0.084. While threshold-based abstention reduced exact-match routing error, Mondrian conformal prediction at α=0.10 yielded high mean positive-label recall coverage (0.883-0.917) with smaller candidate sets and higher precision than threshold baselines. The findings highlight that uncertainty-aware evaluation reveals critical failure modes missed by aggregate metrics, emphasizing that set-valued predictions serve as candidate generation for review, not automated pathway selection.

Key takeaway

For clinical NLP engineers developing decision support systems, integrating uncertainty-aware multi-label classification is crucial. Your models should not rely solely on aggregate metrics, as these can obscure critical missed pathways. Employing techniques like conformal prediction or threshold-based abstention will help you identify uncertain predictions, reducing the risk of excluding important care pathways. Remember that set-valued predictions are best used for generating candidate pathways for human review, not for fully automated routing.

Key insights

Uncertainty-aware multi-label classification of clinical text improves routing to care pathways by exposing critical failure modes.

Principles

• Standard hard-label evaluation can miss clinically important pathways.
• High-recall routing is not cost-free; it increases review burden.
• Set-valued prediction generates candidates, not final selections.

Method

Formulate gastrointestinal report routing as a multi-label uncertainty-aware classification task. Compare models using threshold-based abstention and set-valued conformal prediction. Evaluate hard-routing and downstream review burden.

In practice

• Use ClinicalBERT for strong multi-label clinical text routing.
• Implement min-margin abstention for lower AURC.
• Apply Mondrian CP for high recall coverage and smaller candidate sets.

Topics

• Clinical NLP
• Multi-label Classification
• Uncertainty Quantification
• Conformal Prediction
• ClinicalBERT
• Healthcare AI Routing

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.