---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

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:

python3 Skills/Clinical/Clinical_NLP/entity_extractor.py \
    --text "Patient has diabetes type 2. Prescribed Metformin 500mg. No chest pain." \
    --output entities.json