SepsisSense is an advanced AI-powered system designed to predict sepsis onset in ICU patients 6 hours before it occurs, potentially saving countless lives through early intervention. Built using state-of-the-art machine learning techniques and featuring an intuitive web interface with real-time explainability.
- 🔮 Early Prediction: Predicts sepsis 6 hours in advance with high accuracy
- 📊 Real-time Dashboard: Interactive Streamlit web application
- 🔍 AI Explainability: SHAP-powered feature importance analysis
- ⚡ Fast Processing: Optimized preprocessing and inference pipeline
- 📱 Responsive UI: Modern, animated, and user-friendly interface
- 🩺 Clinical Integration: Ready for healthcare environment deployment
SepsisSense/
├── 📁 app/
│ └── streamlit_app.py # Main web application
├── 📁 data/
│ ├── raw/ # Raw PhysioNet Challenge 2019 data
│ ├── interim/ # Intermediate processing files
│ └── processed/ # Final processed datasets
├── 📁 models/
│ ├── sepsissense_xgb.pkl # Trained XGBoost model
│ └── scaler.pkl # Feature scaler
├── 📁 notebooks/
│ ├── 01_eda.ipynb # Exploratory Data Analysis
│ ├── 02_feature_eng.ipynb # Feature Engineering
│ └── 03_modeling.ipynb # Model Development
├── 📁 src/
│ ├── preprocess.py # Data preprocessing pipeline
│ ├── feature_engineering.py # Feature extraction & engineering
│ ├── train_model.py # Model training pipeline
│ └── explain_model.py # SHAP explainability
├── requirements.txt # Python dependencies
└── README.md # Project documentation
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Clone the repository:
git clone https://github.com/yourusername/SepsisSense.git cd SepsisSense -
Install dependencies:
pip install -r requirements.txt
-
Run the web application:
streamlit run app/streamlit_app.py
- Upload Patient Data: Use CSV upload or manual entry form
- Get Predictions: View real-time sepsis risk (0-100%)
- Understand Results: Examine SHAP explanations for key features
- Monitor Trends: Track patient vitals over time
- Clinical Alerts: Receive notifications when risk exceeds thresholds
| Metric | Score |
|---|---|
| ROC-AUC | 0.87+ |
| PR-AUC | 0.82+ |
| F1-Score | 0.79+ |
| Recall@Precision≥0.8 | 0.75+ |
| Prediction Window | 6 hours |
- Missing Value Handling: Forward/backward fill per patient timeline
- Feature Normalization: StandardScaler for continuous variables
- Rolling Windows: 6-hour statistical features (mean, std, slope)
- Time-Series Features: Trend analysis for vital signs
- Vital Signs: Heart Rate, MAP, SpO₂, Temperature, Respiration Rate
- Laboratory Values: WBC, Platelets, Lactate, Creatinine, Bilirubin
- Demographics: Age, Gender, ICU Type, Admission Source
- Temporal Features: Time since admission, hour of day, day of week
- Primary Model: XGBoost with class imbalance handling
- Alternative: LightGBM for faster inference
- Optional: LSTM/GRU for sequential patterns
- Explainability: SHAP TreeExplainer for feature importance
- Risk Gauge: Color-coded sepsis probability meter
- Feature Importance: Interactive SHAP value charts
- Vital Trends: Real-time patient monitoring graphs
- Alert System: Threshold-based clinical notifications
- Data Upload: Drag-and-drop CSV or manual entry
- Modern Styling: Custom CSS with healthcare color schemes
- Animations: Smooth transitions and loading effects
- Responsiveness: Mobile-friendly design
- Accessibility: Screen reader support and keyboard navigation
- Caching: Streamlit cache for model loading and predictions
- Vectorization: NumPy/Pandas optimized operations
- Model Compression: Optimized pickle serialization
- Memory Management: Efficient data structures for large datasets
- Data Privacy: No patient data storage by default
- HIPAA Considerations: Secure data handling protocols
- Audit Trails: Prediction logging for clinical review
- Input Validation: Robust data sanitization
- Unit Tests: Comprehensive test suite for all modules
- Integration Tests: End-to-end pipeline validation
- Model Validation: Cross-validation and holdout testing
- Clinical Validation: Healthcare professional review process
- API Documentation: Detailed function and class references
- Architecture Guide: System design and component interactions
- Deployment Guide: Production environment setup
- Clinical Guide: Medical interpretation of predictions
- Feature Explanations: Clinical significance of model inputs
- Alert Protocols: Recommended response to high-risk predictions
We welcome contributions from the healthcare AI community! Please see our Contributing Guidelines for details.
# Install development dependencies
pip install -r requirements-dev.txt
# Run tests
pytest tests/
# Run linting
flake8 src/ app/
black src/ app/This project is licensed under the MIT License - see the LICENSE file for details.
- PhysioNet Challenge 2019: For providing the sepsis detection dataset
- Healthcare Partners: For clinical validation and feedback
- Open Source Community: For the amazing ML and visualization libraries
- Issues: GitHub Issues
- Documentation: Wiki
- Email: sepsissense@healthcare-ai.com
Built with ❤️ for better healthcare outcomes