Cross Lingual Embeddings for Clinical Text: A Statistical Framework for Validating Real and Synthetic Electronic Health Records

Abstract

The effective integration of real and synthetic clinical data in multiple languages is essential to advance healthcare research. In this study, we propose a statistical framework that leverages cross-lingual embeddings to validate semantic alignment between authentic Italian EHRs and synthetic English clinical notes. Using two state-of-the-art models, E5 and BGE, we encode the texts and employ Fuzzy C-Means clustering along with multidimensional scaling to assess their semantic coherence. Our analysis reveals distinct language-specific patterns alongside robust cross-lingual alignment, highlighting the promise of synthetic data augmentation in mitigating resource scarcity.