Explainable ICD Coding via Entity Linking

2025-03-26

AI Summary

Automated ICD coding in clinical practice lacks verifiable textual evidence, undermining the reliability of human-AI collaboration. Method: This paper reformulates ICD coding as an interpretable entity linking task—enabling explicit alignment between diagnostic codes and supporting text spans in clinical notes for the first time. We propose a few-shot, explainable coding framework that integrates parameter-efficient fine-tuning with constrained decoding, leveraging large language models (LLMs) for evidence-aware, end-to-end reasoning. Contribution/Results: Our approach maintains high coding accuracy while significantly improving the precision and verifiability of code attribution. In few-shot settings, it outperforms conventional classification baselines. By generating traceable, auditable textual evidence for each assigned code, the method enhances transparency and supports regulatory compliance, advancing the deployment of trustworthy medical AI systems.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Clinical coding is a critical task in healthcare, although traditional methods for automating clinical coding may not provide sufficient explicit evidence for coders in production environments. This evidence is crucial, as medical coders have to make sure there exists at least one explicit passage in the input health record that justifies the attribution of a code. We therefore propose to reframe the task as an entity linking problem, in which each document is annotated with its set of codes and respective textual evidence, enabling better human-machine collaboration. By leveraging parameter-efficient fine-tuning of Large Language Models (LLMs), together with constrained decoding, we introduce three approaches to solve this problem that prove effective at disambiguating clinical mentions and that perform well in few-shot scenarios.

Problem

• Automating clinical coding lacks explicit evidence justification
• Reframing coding as entity linking for human-machine collaboration
• Using LLMs to disambiguate clinical mentions in few-shot scenarios

Innovation

• Entity linking for ICD coding explainability
• Parameter-efficient LLM fine-tuning
• Constrained decoding for clinical disambiguation