Semantic Text Vectorization in Healthcare: Transformer-Based Foundations and Benefit Potentials

Unstructured clinical text in electronic health records contains essential patient information but is difficult to reuse systematically due to limited semantic interpretability beyond keyword search.

This paper presents the theoretical foundations of transformer-based text vectorization and its benefit potentials for semantic analysis and secondary use of clinical free text.

A conceptual framework is described in which transformer models generate context-sensitive semantic vector representations of clinical narratives, enabling advanced analyses such as semantic retrieval and similarity-based comparison.

Transformer-based embeddings support meaning-oriented access to clinical text, automated document structuring, case similarity analysis, and semantic linking across heterogeneous sources, extending classical retrieval and rule-based approaches.

Transformer-based text vectorization provides a scalable semantic layer for unstructured clinical documentation and supports systematic secondary use when integrated with appropriate validation, bias control, and governance mechanisms.