--> --- 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 --- The **Clinical NLP Skill** converts free-text clinical notes into structured data. It identifies key medical entities like problems/diagnoses, medications, a...
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---
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
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
```
```
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
