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Clinical nlp extractor

Skill FridrichMethod/awesome-skills/skills/clinical-nlp-extractor

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Install
npx -y skills add FridrichMethod/awesome-skills --skill clinical-nlp-extractor

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What its author says it does

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

SKILL.md

1.8 KB, as published. Nobody here has run it

<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <[email protected]> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

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


<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

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