Context-Aware Hierarchical Bayesian Modeling of IVF Laboratory Environmental Conditions

Zahra Asghari Varzaneh, Reza Khoshkangini, Pia Saldeen, Lars Johansson, Thomas Ebner

Keywords: Context-Aware, Hierarchical Bayesian, pregnancy rates, IVF

Abstract:

IVF pregnancy rates are routinely modeled using patient-level variables, while high-resolution
laboratory environmental data remain underutilized-
lized. 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.

Track: Track 2: ML Research by Muslim Authors

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Submission Number: 48