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
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
1---2name: clinical-nlp-extractor3description: <!--4---5<!--6# COPYRIGHT NOTICE7# This file is part of the "Universal Biomedical Skills" project.8# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>9# All Rights Reserved.10#11# This code is proprietary and confidential.12# Unauthorized copying of this file, via any medium is strictly prohibited.13#14# Provenance: Authenticated by MD BABU MIA1516-->1718---19name: 'clinical-nlp-extractor'20description: 'Extracts medical entities (Diseases, Medications, Procedures) from unstructured clinical text using regex and simple rules (or LLM wrappers).'21measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.22allowed-tools:23 - read_file24 - run_shell_command25---262728# Clinical NLP Entity Extractor2930The **Clinical NLP Skill** converts free-text clinical notes into structured data. It identifies key medical entities like problems/diagnoses, medications, and procedures.3132## When to Use This Skill3334* When analyzing unstructured EHR notes.35* To populate a patient's problem list or medication reconciliation.36* To de-identify text (phi-removal) - *Basic version*.3738## Core Capabilities39401. **NER (Named Entity Recognition)**: Extracts Problems, Drugs, Procedures.412. **Negation Detection**: (Basic) Checks if a finding is denied ("No fever").423. **Structuring**: Returns JSON format compatible with FHIR/USDL.4344## Workflow45461. **Input**: A string of clinical text or a text file.472. **Process**: Tokenizes and matches against patterns/dictionaries.483. **Output**: JSON list of entities with spans and types.4950## Example Usage5152**User**: "Extract entities from this note."5354**Agent Action**:55```bash56python3 Skills/Clinical/Clinical_NLP/entity_extractor.py \57 --text "Patient has diabetes type 2. Prescribed Metformin 500mg. No chest pain." \58 --output entities.json59```6061```6263<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
Run npx skillmds@latest add biotender-max/clinical-nlp-extractor in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.
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BioTender-max (@biotender-max) published this skill. Their other Agent Skills are listed on their SkillMD profile.
BioTender-max