Validate Aizen platform

Sample application

After infrastructure and core components are installed and all pods are in Running state, launch a sample health risk use case

Healthcare workflow

This usecase validates an AI-driven healthcare workflow that continuously monitors patients, with heart-risk using predictive models and triggers timely intervention when risk patterns are detected. The system evaluates patients health data to identify anomalies or deterioration, engages patients through automated messaging to gather symptom and medication adherence updates, and generates summaries for doctors. Doctors can then view the situation, communicate with the patient, and adjust treatment if required. The workflow ensures that all the interactions and updates are recorded, enabling a closed-loop process that supports early detection, faster response and improved patient care outcomes

For machine learning (ML) pipeline

• Download health_risk demo usecase by downloading the compressed tar file

• Upload the JSON file health_risk_demo_norealtime

  • Login to Aizen platform via the application url http(s)://<KUBERNETES CLUSTER INGRESS HOST>/aizen/gui

  • Go to Platform -> Projects -> Create project -> Upload JSON

  • Enter project description (validating ML pipeline)

  • Click Next -> Next –> Create New Project

• Now you will notice the project has been created in studio of Foresight, ML pipeline got created using the uploaded JSON file

• Go to Foresight -> Studio

• Double click on the pipeline which will take you to studio, where you can actually run the different stages of the ML pipeline