Lab guide for Early Diagnostics Detector¶

Overview¶

This lab is written to simulate an early diagnostics scenario in the healthcare domain. The solution uses IBM's data and AI platform to ingest patient records, analyse risk signals, and surface early-stage diagnostic insights through an intelligent application interface.

Coming Soon

This lab guide is currently being developed. The full step-by-step instructions, reference architecture, and suggested scripts will be published here shortly. Check back later.

Pre-requisites¶

Make sure you've already set up the environment:
- Lab Environment Setup (coming soon)
Access to the relevant IBM product environments (details to follow)

Reference Architecture¶

Reference architecture diagram coming soon.

Key Components¶

Data Ingestion Pipeline – Ingests structured patient records and real-time signals into a unified data layer.
AI Model Service – Trained classification model that scores patient risk based on historical and current data.
Diagnostic Dashboard – An intelligent application surface that presents risk scores and recommended actions to clinicians.
Governance Layer – Tracks model lineage, drift, and bias to ensure compliance with healthcare regulations.

Steps¶

1. Set up the data environment¶

Steps coming soon.

2. Deploy the AI model¶

Steps coming soon.

3. Configure the diagnostic application¶

Steps coming soon.

4. Test the solution¶

Steps coming soon.

Suggested script¶

Suggested interaction scripts and expected outputs will be provided here.

Conclusion

👏 Congratulations on completing the lab! 🎉