Discovery and inference beyond linearity for epidemiological data by integrating Bayesian regression, tree ensembles and Shapley values
Researchers have developed a new framework called RuleSHAP to improve statistical inference for machine learning models in epidemiology. This framework integrates Bayesian regression, tree ensembles, and Shapley values to provide uncertainty quantification for feature effects, which is often lacking in current ML applications. RuleSHAP can detect nonlinear and interaction effects, offering individual-level uncertainty estimates, and has been demonstrated on simulated data and an epidemiological cohort to identify effects related to high cholesterol and blood pressure. AI
IMPACT Enhances the reliability of machine learning models for discovering health risk factors and improving epidemiological research.