Context-Aware Hierarchical Bayesian Modeling of IVF Laboratory Environmental Conditions
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 env...
| Authors | Zahra Asghari Varzaneh, Reza Khoshkangini, Pia Saldeen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.