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Serices List
NLP Annotations in Medical Domain
Natural Language Processing (NLP) annotations in the medical field help structure unstructured clinical text, making it usable for AI models, research, and healthcare applications. Here are key types of NLP annotations along with examples:
- Medical Image Annotation
- Medical Text and Audio Processing
- Medical Video Annotation
- Waveform Analysis and Annotation
- Medical Coding Services
Medical Image Annotation
- Named Entity Recognition (NER) - Identifies and classifies key medical terms in text.
- Clinical Concept Mapping - Maps medical terms to standardized ontologies like SNOMED CT, ICD-10, or UMLS.
- Entity Linking & Disambiguation - Links extracted entities to medical databases to resolve ambiguities.
- Relation Extraction - Identifies relationships between medical entities.
- De-identification (PHI Anonymization) - Removes or masks Protected Health Information (PHI) to ensure compliance.
- Sentiment & Risk Assessment Annotation - Analyzes patient records for sentiment or risk factors.
- Symptom & Treatment Extraction - Extracts symptoms and their corresponding treatments from medical records.
- Text Summarization for Clinical Notes - Extracts key information from long clinical notes.