IVF 妊娠率通常使用患者水平变量, 进行建模，而高分辨率实验室环境数据仍未得到充分利用。我们表明这是一个错失的机会。我们不依赖原始传感器平均值,，而是设计了 55 个上下文感知时间特征,，包括滚动热稳定性, 同时温度-湿度依从性, 峰值应力持续时间, 和应力后恢复速度,，以捕获培养箱微环境的动态。根据来自亚洲 IVF 诊所, 的 61 周数据，这些特征将交叉验证的预测误差降低至 1.27%,，而原始平均值为 3-5%。然后，我们训练一个分层贝叶斯 Beta 回归模型，该模型通过部分池, 共享亚洲和北欧诊所的环境影响，同时保留特定地点的基线。根据来自北欧诊所, 的保留数据，该模型实现了 R2 = 0.86，并且与初始基线, 相比，35-39 岁年龄组的误差减少了 64%，这表明结构化环境监测包含有临床意义的, 可转移信号。
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.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)