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Clinical Nlp Extractor

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clinical-nlp-extractor

Clinical Nlp Extractor is an Agent Skill for documents published in the majiayu000/claude-skill-registry repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Extracts medical entities (Diseases, Medications, Procedures) from unstructured clinical text using regex and simple rules (or LLM wrappers).

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/data/clinical-nlp/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/clinical-nlp-extractor, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

---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