Post Hoc Agentic Refinement for Improving Precision in Multilingual Clinical Text De-identification

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

Summary

A new agentic refinement system is proposed to enhance precision in multilingual clinical text de-identification, addressing the issue of excessive over-tagging that reduces data utility while prioritizing privacy. This system employs an agentic refiner that reviews high-recall annotations using lightweight tools, including validation functions, adaptive context retrieval, persistent to-do state, and modular review skills. Experiments across three multilingual datasets demonstrate significant improvements in binary precision. Furthermore, a synthetic error dataset of common failure modes was introduced, on which the agent successfully corrected 99% of injected errors in medical datasets. This agent-based refinement offers a flexible and effective modular extension for existing high-recall de-identification systems.

Key takeaway

For NLP engineers and research scientists working on clinical text de-identification, where balancing privacy protection with data utility is crucial, you should consider integrating agent-based refinement. This approach offers a flexible and effective modular extension to existing high-recall systems, significantly improving precision without compromising recall. Evaluate its application, especially for multilingual datasets, to enhance the quality of your de-identified data.

Key insights

An agentic refiner improves de-identification precision by reviewing high-recall annotations with lightweight tools.

Principles

• Prioritize recall for privacy protection.
• Over-tagging reduces data utility.
• Agent-based refinement enhances precision.

Method

An agentic refiner reviews high-recall annotations using validation functions, adaptive context retrieval, persistent to-do state, and modular review skills to improve precision.

In practice

• Integrate agentic refiners into existing systems.
• Use synthetic error datasets for analysis.
• Apply lightweight tools for annotation review.

Topics

• Clinical Text De-identification
• Agentic Refinement
• Multilingual NLP
• Precision-Recall
• Medical Data Privacy
• Annotation Quality

Best for: AI Scientist, NLP Engineer, Research Scientist

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