Problem
Research questions and friction points this paper is trying to address.
Automatic medical coding
Long-tail prediction
Imbalanced label distributions
Rare codes
CoLa-ICD: A Knowledge-Enhanced Framework for Long-Tail Automated Medical Coding
🤖 AI Summary
本文提出CoLa-ICD框架，通过增强知识、建模相关代码依赖关系及学习标签语义与临床证据间更强对齐来解决长尾自动医疗编码问题。
📝 Abstract
Automatic medical coding assigns ICD codes to clinical notes, but it remains challenging due to long documents, imbalanced label distributions, and diverse terms. These challenges are especially severe for rare codes, which have limited training instances and are easily confused with semantically similar labels. We introduce CoLa-ICD, a knowledge-enhanced framework for long-tail prediction. CoLa-ICD enriches ICD labels with external terms, models dependencies among related codes, and learns stronger alignment between label semantics and clinical evidence for long-tail prediction. Experiments show that CoLa-ICD improves long-tail prediction with larger gains in larger and sparser label spaces and achieves state-of-the-art performance in AUC, F1, and P@k. Our code is available at https://github.com/youwillbethebest/Cola-ICD.
Innovation
Methods, ideas, or system contributions that make the work stand out.
knowledge-enhanced
long-tail prediction
external terms
dependencies among codes
label semantics
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