深度模型预测血糖时忽略胰岛素等关键因素，作者提出量化这一问题并给出改进方向。
The Driver-Blindness Phenomenon: Why Deep Sequence Models Default to Autocorrelation in Blood Glucose Forecasting
- 通过对比多变量与单变量模型性能差值，定义并量化了'忽视驱动因素'现象。
- 实证发现多数模型在加入临床数据后性能提升几乎为零，表明存在严重信息浪费。
- 提出特征编码、因果正则化等方法缓解该问题，呼吁报告性能差值以避免误导。
用于血糖预测的深度序列模型始终无法有效利用胰岛素、进餐和活动等临床相关驱动因素，尽管其生理机制已明确。我们称此现象为「驱动盲区」，并用Δ_{ ext{drivers}}（多变量模型相对于匹配的单变量基线的性能提升）进行形式化。文献中Δ_{ ext{drivers}}通常接近于零。我们归因于三个相互作用的因素：倾向于自相关性的模型架构偏差（C1）、使驱动因素噪声大且混杂的数据保真度缺口（C2），以及削弱群体级模型效果的生理异质性（C3）。本文综合了部分缓解驱动盲区的策略，包括生理特征编码器、因果正则化和个人化方法，并建议未来研究常规报告Δ_{ ext{drivers}}，防止盲目模型被误认为先进水平。
原文摘要 · Abstract (English)
Deep sequence models for blood glucose forecasting consistently fail to leverage clinically informative drivers--insulin, meals, and activity--despite well-understood physiological mechanisms. We term this Driver-Blindness and formalize it via $Δ_{\text{drivers}}$, the performance gain of multivariate models over matched univariate baselines. Across the literature, $Δ_{\text{drivers}}$ is typically near zero. We attribute this to three interacting factors: architectural biases favoring autocorrelation (C1), data fidelity gaps that render drivers noisy and confounded (C2), and physiological heterogeneity that undermines population-level models (C3). We synthesize strategies that partially mitigate Driver-Blindness--including physiological feature encoders, causal regularization, and personalization--and recommend that future work routinely report $Δ_{\text{drivers}}$ to prevent driver-blind models from being considered state-of-the-art.
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