Ventilator Failure Prediction and Alert System
The system forecasts ventilator failures by analyzing performance data with machine learning. It monitors failure risks in real-time through Grafana, sending alerts via Prometheus when failure probabilities surpass a predefined threshold.
Click here to view the demo: medicomachineries.tech4bizsolutions.com
Implementation Highlights
- Load the CSV file with ventilator performance data and the trained predictive model using libraries such as pandas and joblib.
- Process the data and input it into the model to generate predictions about potential ventilator failures.
- Develop a Prometheus exporter to expose the prediction outcomes and associated model statistics as metrics.
- Configure a Grafana dashboard to visualize ventilator failure predictions, probabilities, and operational trends in real time.
- Set up Prometheus alert rules to detect when failure probabilities surpass a set threshold or when abnormal behavior is observed.
Integrate notification services like email, Slack, or SMS through Prometheus Alertmanager to send alerts for critical failure predictions
Outcome
Improved patient safety through early identification of potential ventilator failures, ensuring continuous, reliable operation.
Reduced equipment downtime and more efficient resource management by addressing issues before failure occurs.