# Clinical Nlp Extractor > --- name: 'clinical-nlp-extractor' description: 'Extracts medical entities (Diseases, Medications, Procedures) from unstructured clinical text using regex and simple rules (or LLM wrappers).' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # Clinical NLP Entity Extractor The **Clinical NLP Skill** converts free-text clinical notes into structured data. It identifies key medical entities like problems/diagnoses, medications, and procedures. ## When to Use This Skill * When analyzing unstructured EHR notes. * To populate a patient's problem list or medication reconciliation. * To de-identify text (phi-removal) - *Basic version*. ## Core Capabilities 1. **NER (Named Entity Recognition)**: Extracts Problems, Drugs, Procedures. 2. **Negation Detection**: (Basic) Checks if a finding is denied ("No fever"). 3. **Structuring**: Returns JSON format compatible with FHIR/USDL. ## Workflow 1. **Input**: A string of clinical text or a text file. 2. **Process**: Tokenizes and matches against patterns/dictionaries. 3. **Output**: JSON list of entities with spans and types. ## Example Usage **User**: "Extract entities from this note." **Agent Action**: ```bash python3 Skills/Clinical/Clinical_NLP/entity_extractor.py \ --text "Patient has diabetes type 2. Prescribed Metformin 500mg. No chest pain." \ --output entities.json ``` ```