machine learning/clustering/selection

Objective: This paper introduced Bayesian Kernel Machine Regression (BKMR), a novel statistical and machine learning method designed to estimate the complex, non-linear, and interactive health effects of multi-pollutant mixtures (e.g., air pollutants or chemicals).
Key Strengths: BKMR overcomes major limitations of traditional models by effectively handling:
1. Non-linearity in the exposure-response relationship.
2. Complex interactions between pollutants.
3. High collinearity among the mixture components.
Key Output: The Bayesian framework provides flexible estimates of the overall mixture effect and the relative importance of individual pollutants through Posterior Inclusion Probabilities (PIPs), which helps identify the main drivers of the health outcome within the mixture.