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, and React frontend for visualization. The ML component uses 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, and 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, and 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, and balancing between immediate rule-based alerts and predictive ML-based warnings for medical applications.

What's next for MediGuard AI - Real-time Patient Vital Monitoring System 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, and achieving HIPAA compliance for production deployment.

Built With

• fastapi
• griddb-cloud
• numpy
• pandas
• python
• react
• scikit-learn
• tailwindcss
• typescript
• websocket