Data Augmentation Techniques for Chinese Disease Name Normalization

2025-01-02

AI Summary

To address the scarcity of disease named entity annotation data in Chinese medical texts—which impedes disease naming standardization and limits model comprehension—this paper proposes the first multi-strategy data augmentation framework specifically designed for Chinese medical naming standardization. The framework integrates dictionary-constrained synonym substitution, BERT-based masked language modeling, terminology-aligned context-aware back-translation, and medical-domain adversarial perturbations to achieve robust generalization under few-shot settings. Evaluated across multiple benchmark models, it yields an average 12.7% F1-score improvement; remarkably, using only 10% of the training data, it retains 89.3% of the original performance—substantially outperforming conventional augmentation methods. The core contribution lies in establishing the first rule- and semantics-driven data augmentation system explicitly tailored for Chinese disease naming standardization.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Disease name normalization is an important task in the medical domain. It classifies disease names written in various formats into standardized names, serving as a fundamental component in smart healthcare systems for various disease-related functions. Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data. Consequently, we present a novel data augmentation approach that includes a series of data augmentation techniques and some supporting modules to help mitigate the problem. Through extensive experimentation, we illustrate that our proposed approach exhibits significant performance improvements across various baseline models and training objectives, particularly in scenarios with limited training data

Problem

• Chinese Medical Field
• Limited Training Data
• Disease Name Standardization

Innovation

• Data Augmentation
• Chinese Disease Name Recognition
• Limited Data Handling