Audit a Sepsis Early-Warning Model for Subgroup Performance
Overview
What this challenge is about.
Compute AUROC, AUPRC, fairness gaps, and a drift plan for a sepsis model. Deliver a 6-page audit and earn your verifiable certificate.
The scenario
The hospital network (12 hospitals, around 14,000 ICU stays per year) is rolling out the model under their medical-informatics-committee governance and requires a documented audit before go-live.
The Brief
What you'll do, and what you'll demonstrate.
Run a defensible pre-deployment audit of a sepsis early-warning model with discrimination, calibration, fairness, and a monitoring plan.
Earning criteria — what you'll demonstrate
- Audit a clinical early-warning model across multiple axes
- Quantify subgroup gaps in a clinical-safety-relevant way
- Propose mitigations that respect clinical workflow constraints
- Communicate audit findings to a medical-informatics committee
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Machine Learning for Healthcare and Biomedicine
Master · Applied Ai
Strong alignment
This challenge maps to Machine Learning for Healthcare and Biomedicine at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Fairness Metrics
Apply fairness metrics to solve real industry problems and demonstrate production-level capability.
- Model Calibration
Apply model calibration to solve real industry problems and demonstrate production-level capability.
- Drift Detection
Apply drift detection to solve real industry problems and demonstrate production-level capability.
- Risk Stratification
Apply risk stratification to solve real industry problems and demonstrate production-level capability.
- Model Monitoring
Apply model monitoring to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Safety Researcher
Pre-deployment clinical audits paired with monitoring plans are the AI-safety-researcher's signature deliverable at consultancies serving healthcare networks.
This challenge sharpens
- fairness-metrics
- drift-detection
- model-monitoring
Applied AI Scientist
Producing committee-ready audit packages bridging technical evaluation and clinical governance is the applied-AI-scientist's craft at any healthtech consultancy.
This challenge sharpens
- model-calibration
- model-evaluation
- risk-stratification
MLOps Engineer
Designing post-deploy drift monitoring with clinical-action escalation paths is core MLOps work for any production clinical model.
This challenge sharpens
- drift-detection
- model-monitoring
- model-evaluation