A newer version of the Gradio SDK is available:
6.4.0
metadata
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:
- Clinical NER: Extract clinical entities from medical text
- Anatomy Detection: Detect anatomical terms
- POS Tagging: Analyze part-of-speech tags
- 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.