Context-Aware Hierarchical Bayesian Modeling of IVF Laboratory Environmental Conditions

IVF success rates are influenced by countless variables, but the physical conditions inside laboratory incubators — temperature stability, humidity adherence, recovery speed after disturbances — have historically been modeled crudely if at all. This paper demonstrates that richly engineered temporal features from environmental sensors, combined with a hierarchical Bayesian model that pools information across clinics, can predict weekly pregnancy rates with striking accuracy. Beyond IVF, the methodology generalizes to any precision biological process where environmental micromanagement matters, including cell therapy manufacturing, pharmaceutical production, and agricultural biotech, where understanding the dynamics of controlled environments is critical to yield.