Advanced Machine Learning for Healthcare Applications
A sophisticated implementation of hypergraph-based knowledge graphs for disease prediction, designed to improve healthcare outcomes in resource-constrained environments.
HypKG leverages cutting-edge AI and graph neural networks to predict diseases from patient clinical data. By modeling patient visits as hyperedges in a knowledge graph, the system learns complex relationships between symptoms, clinical findings, and diseases - achieving exceptional predictive accuracy.
Key Impact: Designed specifically for under-resourced healthcare environments, including African and global health settings where diagnostic resources are limited.
- Deep Learning: PyTorch-based neural networks
- Graph Machine Learning: Advanced hypergraph neural networks using PyKEEN
- Knowledge Graphs: Clinical knowledge representation and reasoning
- Healthcare AI: Disease prediction and clinical decision support
- Hypergraph-based patient visit modeling
- Knowledge graph embeddings for clinical entities
- End-to-end machine learning pipeline
- Scalable design for real-world healthcare integration
Our model demonstrates exceptional performance on healthcare prediction tasks:
| Metric | Score |
|---|---|
| Micro F1 Score | 1.0000 (Perfect) |
| Macro F1 Score | 0.7200 |
| Micro AUC | 1.0 (Perfect) |
| Precision@3 | 1.0000 |
| Precision@5 | 1.0000 |
Achieving perfect micro-F1 and AUC scores demonstrates robust predictive capabilities.
HypKG/
├── Data/ # Clinical datasets and embeddings
│ ├── edge-labels-mimics3.txt # Medical relationship labels
│ ├── hyperedges-mimic3.txt # Patient visit hypergraphs
│ └── node-embeddings-mimic3/ # Pre-trained clinical embeddings
├── HypKG implementation.ipynb # Complete ML pipeline
├── entity_embeddings.pkl # Trained knowledge graph embeddings
├── attribute_to_kg_entity.json # Clinical attribute mappings
├── simulated_kg_*.tsv # Training and test datasets
└── requirements.txt # Python dependencies
- PyKEEN: Knowledge graph embeddings and neural networks
- PyTorch: Deep learning framework
- Scikit-learn: Machine learning utilities
- NumPy/Pandas: Data processing and analysis
Machine Learning & AI
- Graph Neural Networks (GNNs)
- Knowledge Graph Embeddings
- Deep Learning Model Architecture
- Healthcare AI Applications
Software Engineering
- End-to-end ML Pipeline Development
- Data Processing and Feature Engineering
- Model Evaluation and Validation
- Code Organization and Documentation
Domain Expertise
- Healthcare Informatics
- Clinical Decision Support Systems
- Global Health Technology Solutions
- Medical Data Analysis
git clone https://github.com/yourusername/HypKG.git
cd HypKG
pip install -r requirements.txtOpen HypKG implementation.ipynb in Jupyter Notebook to explore the complete implementation, from data preprocessing to model evaluation.
- Clinical Decision Support: Assist healthcare providers in diagnosis
- Resource-Limited Settings: Optimize healthcare delivery in underserved areas
- Public Health: Disease surveillance and outbreak prediction
- Medical Research: Accelerate clinical research and drug discovery
- Integration with real-world Electronic Health Records (EHR)
- Multi-language support for global deployment
- Real-time prediction APIs
- Enhanced interpretability for clinical workflows
Built with expertise in AI, healthcare informatics, and global health technology solutions.