AI metamodeling for population health prediction: a case study in colorectal cancer prevention planning

We present a framework for constructing individual-level metamodels of complex simulations to support rapid, data-driven decision-making. By training models at the individual level and aggregating predictions, our framework enables flexible representation of any target population and facilitates comparison of alternative strategies. We explore regression-based and machine learning-based approaches and demonstrate the framework’s utility in a colorectal cancer screening case study spanning diverse scenarios and interventions. Outputs such as cancer cases averted and life years lost are accurately predicted, with metamodels closely reproducing simulation results while substantially reducing computational burden. We further show how metamodel outputs can support cost-effectiveness comparisons and guide intervention selection without additional simulation runs. Although demonstrated in a colorectal cancer screening context, the proposed framework can be used in many domains and can be applied to simulations that produce individual-level outputs. Overall, our framework provides a scalable approach for accelerating policy evaluation and strategy comparison across diverse healthcare and operations environments.