---name: clinical-nlp-extractor description: Extracts medical entities (Diseases, Medications, Procedures) from unstructured clinical text using regex and simple rules (or LLM wrappers). keywords:
- nlp
- ner
- clinical-notes
- entity-extraction
- fhir measurable_outcome: Extracts key medical entities (Problems, Meds) with >80% recall on standard synthesized clinical notes. license: MIT metadata: author: AI Group version: "1.0.0" compatibility:
- system: Python 3.10+ allowed-tools:
- run_shell_command
- read_file ---"
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
- NER (Named Entity Recognition): Extracts Problems, Drugs, Procedures.
- Negation Detection: (Basic) Checks if a finding is denied ("No fever").
- Structuring: Returns JSON format compatible with FHIR/USDL.
Workflow
- Input: A string of clinical text or a text file.
- Process: Tokenizes and matches against patterns/dictionaries.
- Output: JSON list of entities with spans and types.
Example Usage
User: "Extract entities from this note."
Agent Action:
python3 Skills/Clinical/Clinical_NLP/entity_extractor.py \
--text "Patient has diabetes type 2. Prescribed Metformin 500mg. No chest pain." \
--output entities.json