用环境动态特征提升试管婴儿成功率预测
Context-Aware Hierarchical Bayesian Modeling of IVF Laboratory Environmental Conditions
- 设计55个上下文感知的时间特征捕捉培养箱微环境变化
- 预测误差降至1.27%，比原始平均值降低60%以上
- 跨地域模型共享环境影响，适合辅助生殖研究者
试管婴儿妊娠率通常基于患者层面变量建模，而高分辨率实验室环境数据仍被低估。本文表明这是被忽视的机会。我们不依赖原始传感器平均值，而是构建了55个上下文感知的时序特征，包括滚动热稳定性、温湿度同步性、峰值压力持续时间及压力后恢复速度，以捕捉培养箱微环境动态。在一家亚洲试管婴儿诊所61周的数据上，这些特征将交叉验证预测误差降至1.27%，远低于原始平均值的3-5%。随后，我们训练了一个分层贝叶斯贝塔回归模型，通过部分池化机制共享亚洲与北欧诊所的环境效应，同时保留各站点特异性基线。在北欧诊所的预留数据上，模型实现R² = 0.86，对35-39岁群体预测误差比朴素基线降低64%，证明结构化环境监测包含可迁移的临床意义信号。
原文摘要 · Abstract (English)
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.
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