Semantic Embedding Alignment for Cross-Institutional Clinical Text Mining

Abstract

This paper explores a unified framework for semantic embedding alignment in cross-institutional clinical text mining contexts. The goal is to ensure that concept representations across different medical institutions retain consistent semantic information despite discrepancies in data collection procedures, annotation guidelines, and linguistic variations in documentation style. The approach focuses on leveraging geometry-aware transformations to map institution-specific embeddings into a common latent space, allowing domain-invariant representations that facilitate downstream clinical tasks. The method addresses challenges arising from heterogeneous data distributions and multi-scale contextual embeddings, providing a foundation for federated clinical studies that respect local privacy constraints. Central to this framework is the concept of establishing topological homomorphisms between embedding spaces, captured through advanced linear algebraic and logical formulations that enable robust alignment even under partial supervision or noisy label constraints. The significance of this methodology lies in its ability to harmonize terminological discrepancies and to reduce the risk of model miscalibration when applying machine learning techniques to sensitive, domain-specific corpora. Through the integration of manifold projections and learned semantic correspondences, the framework promises to facilitate tasks such as clinical named entity recognition, phenotype identification, and automated diagnostic coding, thereby enhancing interoperability and reducing diagnostic gaps across multiple healthcare institutions.