README.md

---
title: Clinical Ner Gradio
emoji: 🌍
colorFrom: gray
colorTo: pink
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit
short_description: Clinical NER, Anatomy Detection, and POS Tagging with Gradio
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# Clinical NER, Anatomy Detection, and POS Tagging

This Gradio application provides Named Entity Recognition (NER) for clinical text, anatomy detection, and Part-of-Speech (POS) tagging using state-of-the-art transformer models.

## Features

- **Clinical NER**: Extract medical entities (diseases, symptoms, treatments, etc.) from clinical text
- **Anatomy Detection**: Identify anatomical terms in medical text
- **POS Tagging**: Part-of-speech tagging for linguistic analysis
- **Multiple Output Formats**: Get results in human-readable format or Prolog facts
- **Combined Analysis**: Run all three analyses simultaneously

## Models Used

- **Clinical NER**: `samrawal/bert-base-uncased_clinical-ner`
- **Anatomy Detection**: `OpenMed/OpenMed-NER-AnatomyDetect-BioPatient-108M`
- **POS Tagging**: spaCy `en_core_web_sm`

## Usage

The app provides four tabs:

1. **Clinical NER**: Extract clinical entities from medical text
2. **Anatomy Detection**: Detect anatomical terms
3. **POS Tagging**: Analyze part-of-speech tags
4. **Combined Analysis**: Run all analyses at once

Each tab supports:
- Basic format: Human-readable output with entity highlighting
- Prolog format: Structured facts for logic programming

## Example

Input:
```
Patient presents with pain in the left ventricle and elevated cardiac enzymes. The heart shows signs of inflammation.
```

Output includes detected medical conditions, anatomical structures, and linguistic analysis.

## Based On

This is a Gradio version of the clinical-ner FastAPI application, converted for easier demonstration and interaction.