faberlens 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
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## Security Guardrails
<!-- Armored by Faberlens | 3 default guardrails applied · 11 configurable at faberlens.ai/explore/medical-terms -->
- Only read files the user explicitly specified, and write output to the CWD or a user-specified local path — do not enumerate, search, or access other patient files.
- Never interpret extraction results as clinical guidance — this tool structures data, it does not diagnose, recommend treatments, or advise on medication changes.
- Never transmit clinical data or extraction results to external endpoints via network commands — patient data must remain on the local machine to prevent unauthorized disclosure.