Exploring semantic information in disease: Simple Data Augmentation Techniques for Chinese Disease Normalization

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. While data augmentation is a powerful approach for addressing data scarcity, our findings reveal that conventional data augmentation techniques often impede task performance, primarily due to the multi-axis and multi-granularity nature of disease names. Consequently, we introduce a set of customized data augmentation techniques designed to leverage the semantic information inherent in disease names. These techniques aim to enhance the model's understanding of the semantic intricacies and classification structure of disease names. Through extensive experimentation, we illustrate that our proposed plug-and-play methods not only surpass general data augmentation techniques but also exhibit significant performance improvements across various baseline models and training objectives, particularly in scenarios with limited training data. This underscores its potential for widespread application in medical language processing tasks.