A comprehensive multi-agent data science pipeline system for oncology applications, specifically focused on cancer detection, analysis, and clinical recommendation generation.
OncologyMLSuite is an enterprise-grade, modular machine learning platform designed to bridge the gap between research ML and clinical practice in oncology. The system employs a multi-agent architecture to handle the complete data science workflow from ingestion to clinical recommendations.
- FastAPI - API serving and agent orchestration
- Redis - Agent coordination, caching, and messaging
- Docker/Kubernetes - Containerization and deployment
- Pydantic - Type safety and data validation
- Data Ingestion Agent - Hospital databases, clinical trials, public datasets
- Preprocessing Agent - Data cleaning, normalization, feature engineering
- Modeling Agent - ML/DL model training and inference
- Evaluation Agent - Statistical analysis and model validation
- Visualization Agent - Interactive dashboards and reporting
- Recommendation Agent - Clinical decision support
- Monitoring Agent - Model drift and system health
- Survival analysis (Kaplan-Meier, Cox regression)
- Bayesian inference and uncertainty quantification
- Multi-modal data support (imaging + genomics)
- Clinical ontology integration (SNOMED CT, ICD-10)
- HIPAA/GDPR compliant data handling
- Model interpretability (SHAP, LIME, Captum)
- Bias detection and mitigation
- Ethical AI safeguards
- Clinician dashboard for predictions and reports
- Data scientist workspace with Jupyter integration
- RESTful API endpoints for automation
- PDF report generation for clinical documentation
# Clone the repository
git clone https://github.com/FCHEHIDI/OncologyMLSuite.git
cd OncologyMLSuite
# Set up the development environment
docker-compose up -d
# Install dependencies
pip install -r requirements.txt
# Run the application
python -m oncology_ml_suite.mainWe use a feature branch workflow:
master- Production-ready codedevelop- Integration branchfeature/*- Individual feature implementations
- Project structure and documentation
- Core agent framework
- Data ingestion pipeline
- Preprocessing modules
- ML model training system
- Statistical analysis tools
- Visualization dashboard
- Clinical recommendation engine
- Monitoring and alerting
- Deployment infrastructure
Please read our contributing guidelines and follow the established branch workflow.
This project is licensed under the MIT License - see the LICENSE file for details.
- Fares Chehidi - @FCHEHIDI
- Email: fareschehidi7@gmail.com
- GitHub: @FCHEHIDI