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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

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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

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