Context-Aware Hierarchical Bayesian Modeling of IVF Laboratory Environmental Conditions
For IVF clinics, this demonstrates that structured environmental monitoring provides clinically meaningful, transferable signal to improve pregnancy rate predictions.
The paper shows that context-aware temporal features from IVF incubator environments reduce prediction error to 1.27% (vs. 3-5% for raw averages) and that a hierarchical Bayesian model transfers environmental effects across clinics, achieving R²=0.86 and 64% error reduction for the 35-39 age group.
IVF pregnancy rates are routinely modeled using patient-level variables, while high-resolution laboratory environmental data remain underutilized. We show that this is a missed opportunity. Rather than relying on raw sensor averages, we engineer 55 context-aware temporal features, including rolling thermal stability, simultaneous temperature-humidity adherence, peak stress duration, and post-stress recovery speed, that capture the dynamics of incubator microenvironments. On 61 weeks of data from an Asian IVF clinic, these features reduce cross-validated prediction error to 1.27%, compared to 3-5% for raw averages. We then train a hierarchical Bayesian Beta regression model that shares environmental effects across an Asian and a Northern European clinic via partial pooling, while preserving site-specific baselines. On held-out data from the Northern European clinic, the model achieves R2 = 0.86 and a 64% error reduction for the 35-39 age group over a naive baseline, demonstrating that structured environmental monitoring contains clinically meaningful, transferable signal.