# AINS6101: Predictive Analytics in Population Health

**Aurnova MSAI track:** Healthcare AI  
**Credits:** 3  
**Format:** 8-week online graduate course

Uses AI and statistical models for risk stratification, forecasting, interventions, equity, and public-health governance.

This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.

## Course Outcomes

By the end of the course, students will be able to:

- explain the major concepts and tradeoffs in Predictive Analytics in Population Health;
- build or evaluate applied AI artifacts aligned with the course domain;
- document assumptions, evidence, limitations, and operational risks;
- connect technical work to governance, stakeholder needs, and deployment readiness.

## Module Map

1. **Population health data ecosystems** — What data sources describe health at population scale?
2. **Risk stratification and prediction** — How are patients grouped for intervention?
3. **Causal inference and confounding** — Why is prediction not the same as cause?
4. **Forecasting and surveillance** — How do models support trend detection?
5. **Equity and social determinants** — How do models account for structural health drivers?
6. **Intervention targeting and evaluation** — How should interventions be prioritized and measured?
7. **Privacy and public-health governance** — What safeguards are required for sensitive population data?
8. **Population analytics portfolio** — What evidence supports a public-health recommendation?