Context-Aware Hierarchical Bayesian Modeling of IVF Laboratory Environmental Conditions

The article presents a new hierarchical Bayesian Beta regression model that incorporates 55 context-aware temporal features derived from high-resolution laboratory environmental data to improve IVF pregnancy rate predictions. The model, trained on 61 weeks of data from clinics in Asia and Northern Europe, significantly reduces cross-validated prediction error to 1.27%, outperforming traditional raw averages (3-5% error) and achieving an R² of 0.86 on held-out data. This approach highlights the importance of structured environmental monitoring in enhancing predictive accuracy for IVF outcomes, offering valuable insights for practitioners in reproductive medicine and data-driven healthcare.