The findings from this study have broader implications for medical language processing tasks beyond just disease name normalization. The novel data augmentation techniques developed could be adapted and applied to various other healthcare-related NLP tasks such as symptom detection, diagnosis prediction, entity linking, and clinical text classification. By leveraging structural invariance and hierarchy properties within medical terms similar to those explored in this study, models can gain a deeper understanding of complex healthcare concepts leading to improved performance across a range of medical NLP applications.