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