Azure-Language-Text-Analytics-for-Health

Azure Language adds advanced natural language processing to your apps using task‑optimized AI models. It helps you extract key information from text, transcripts, and files as well as detect language to build multilingual, conversational experiences—all with enterprise‑grade security and flexible customization.

About this model

The Text Analytics for Health model in Azure Language extracts and labels relevant medical information from unstructured clinical text. It performs named entity recognition, relation extraction, entity linking, and assertion detection to surface structured insights from doctors' notes, discharge summaries, clinical documents, and electronic health records. It supports both real-time and batch processing, making it suitable for a wide range of healthcare and life sciences workflows.

Key model capabilities

• Named Entity Recognition: Identifies medical entities such as diagnoses, medications, symptoms/signs, body structures, dosages, and Social Determinants of Health (SDOH).
• Relation Extraction: Detects semantic relationships between medical entities (e.g., medication–dosage, diagnosis–body structure, treatment–condition).
• Entity Linking: Maps recognized entities to standardized medical ontologies via the Unified Medical Language System (UMLS) for interoperability.
• Assertion Detection: Identifies negation, uncertainty, conditionality, and association in clinical text to correctly contextualize extracted entities.
• FHIR Support: Returns results in the Fast Healthcare Interoperability Resources (FHIR 4.0.1) format for seamless EHR integration.
• Multilingual Support: Processes clinical text in English and additional preview languages for global healthcare applications.