---
title: Docmap Leads Classifier
emoji: 🌖
colorFrom: gray
colorTo: pink
sdk: gradio
sdk_version: 5.44.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: AI-powered healthcare leads classification using DeBERTa-v3-
---

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

# DocMap Healthcare Leads Classifier

AI-powered classification of healthcare leads and patient inquiries using DeBERTa-v3-base.

## 🚀 Features

- **Single Lead Classification**: Classify individual patient inquiries
- **Batch Processing**: Handle multiple leads at once
- **Priority Assessment**: Determine urgency and priority levels
- **Specialty Routing**: Identify required medical specialties
- **Confidence Scores**: Transparent probability outputs

## 🏥 Use Cases

- **Patient Triage**: Prioritize urgent vs. routine cases
- **Specialty Routing**: Direct patients to appropriate departments
- **Lead Qualification**: Assess patient inquiry quality and urgency
- **Resource Planning**: Understand demand patterns

## 🔧 Technical Details

- **Model**: DeBERTa-v3-base fine-tuned for healthcare leads
- **Input**: Patient inquiry text (max 512 tokens)
- **Output**: Classification with confidence scores
- **Performance**: Fast inference on CPU/GPU

## 📊 Classification Categories

The model classifies leads into multiple categories including:
- Priority levels (low, medium, high, emergency)
- Specialty requirements
- Urgency indicators
- Patient type classification

## 🎯 Usage

1. **Single Lead**: Enter one patient inquiry for immediate classification
2. **Batch Processing**: Process multiple leads at once for efficiency
3. **Examples**: Use provided examples to understand input formats

## 📝 Input Format

Describe the patient's:
- Symptoms or condition
- Urgency level
- What they're seeking
- Any relevant medical history

## 🎯 Output

- Primary classification with confidence
- All class probabilities
- Formatted for easy reading
- Professional healthcare presentation

## 🔒 Privacy & Security

- No patient data is stored
- All processing is done in memory
- Secure inference environment
- Compliant with healthcare privacy standards

## 📞 Support

For technical support or questions about the classifier, contact the DocMap team.

---

*Powered by DeBERTa-v3-base and HuggingFace Spaces*