Medical Language Model
A specialized healthcare language model with digital information mapping
Model Architecture
This system is built on a fine-tuned BioGPT model (cmtopbas/biogpt-medical-kg-selfcontained3), specifically trained on medical knowledge graphs to understand and respond to healthcare-related queries with RAG validation.
Technical Details
- Base Model: gpt2-medical-demo
- Running On: CPU (simulated)
- Training Status: Not trained yet
Performance Metrics
No evaluation metrics available yet.
Medical Knowledge Graph
The model is integrated with a comprehensive medical knowledge graph containing 4,388 nodes across 7 entity categories:
Diseases & Symptoms
Comprehensive disease profiles with associated symptoms
Treatments & Tests
Medical treatments, diagnostic tests, and procedures
Body Parts & Cells
Anatomical structures and cellular components
Entity Categories:
- Disease - Medical conditions and disorders
- Symptom - Clinical manifestations
- Treatment - Therapeutic interventions
- DiagnosticTest - Laboratory and imaging tests
- BodyPart - Anatomical structures
- CellularStructure - Cellular components
- RiskFactor - Disease risk factors
Knowledge Graph Statistics
4,388
Medical Entities
7
Entity Categories
RAG
Validation System
Important Disclaimers
This model is for informational purposes only.
The information provided by this language model should not be considered medical advice. Always consult with qualified healthcare professionals for medical diagnoses, treatment options, and advice.