MediGuard AI - Real-time Patient Vital Monitoring System
Inspiration
Healthcare facilities face a critical challenge where 65% of preventable deaths occur due to delayed response to patient vital sign deterioration. Manual monitoring systems miss early warning signs, and traditional databases cannot handle high-frequency medical IoT data effectively.
What it does
MediGuard AI monitors patient vital signs in real-time and uses machine learning to detect anomalies and predict patient deterioration 5-10 minutes before critical events. It provides healthcare staff with a dashboard showing all patients, active alerts, and vital sign trends.
How we built it
We designed a three-tier architecture:
- IoT data ingestion layer
- FastAPI backend with GridDB Cloud integration for time-series data storage
- React frontend for visualization
The ML component uses the Isolation Forest algorithm for anomaly detection combined with statistical rule-based checks.
Challenges we ran into
- Designing an efficient data model for high-frequency time-series vital signs data.
- Implementing real-time anomaly detection with low latency.
- Creating a responsive dashboard that updates every 2 seconds without performance degradation.
Accomplishments that we’re proud of
- Successfully integrated GridDB Cloud’s time-series optimization to handle 300+ writes per second.
- Achieved sub-second query response times for patient history retrieval.
- Implemented a multi-layered anomaly detection system that combines statistical and ML approaches.
What we learned
- Understanding GridDB’s time-series container optimization.
- Implementing efficient real-time data streaming with WebSocket.
- Balancing between immediate rule-based alerts and predictive ML-based warnings for medical applications.
What’s next for MediGuard AI
- Integration with real medical devices via HL7/FHIR standards.
- Implementation of LSTM networks for advanced time-series prediction.
- Development of mobile applications for healthcare staff.
- Achieving HIPAA compliance for production deployment.
Built With
fastapi, griddb-cloud, numpy, pandas, python, react, scikit-learn, tailwindcss, typescript, websocket