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

Area of Science:

• Natural Language Processing
• Biomedical Informatics
• Machine Learning

Background:

• Unstructured clinical text in electronic health records (EHRs) holds vital patient data.
• Current methods struggle with systematic reuse due to limited semantic understanding beyond keyword searches.

Purpose of the Study:

• To present the theoretical basis of transformer-based text vectorization for clinical free text.
• To explore the potential benefits for semantic analysis and secondary data utilization.

Main Methods:

• Describing a conceptual framework utilizing transformer models.
• Generating context-sensitive semantic vector representations of clinical narratives.

Main Results:

• Transformer embeddings facilitate meaning-oriented access to clinical text.
• Enabling automated document structuring, case similarity analysis, and semantic linking.
• Extending capabilities beyond traditional retrieval and rule-based systems.

Conclusions:

• Transformer-based text vectorization offers a scalable semantic layer for clinical documentation.
• Supports systematic secondary use when combined with validation, bias control, and governance.
• Enhances the interpretability and utility of unstructured clinical data.