# Clinical Nlp Extractor

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

- Skill: `fridrichmethod/clinical-nlp-extractor` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add fridrichmethod/clinical-nlp-extractor`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fridrichmethod/clinical-nlp-extractor/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: FridrichMethod (https://skillmd.com/u/fridrichmethod)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/fridrichmethod/clinical-nlp-extractor

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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**:
```bash
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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