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The Driver-Blindness Phenomenon: Why Deep Sequence Models Default to Autocorrelation in Blood Glucose Forecasting

Heman Shakeri

TL;DR

The paper addresses Driver-Blindness in blood glucose forecasting, where deep sequence models underutilize informative drivers (insulin, meals, activity), yielding $\Delta_{\text{drivers}}$ near zero. It introduces an arousal–conspecificity framework and documents three interrelated failure modes—architectural bias (C1), fidelity gaps (C2), and personalization gaps (C3)—supported by cross-method evidence across Gaussian processes, RNNs, CNNs, Transformers, and TFT variants. It reviews mitigation strategies such as physiological encoder representations (IOB/COB/RaG), causal regularization, and personalization, noting that gains are partial (roughly 5–20% RMSE reduction by 60 minutes, often smaller at 30 minutes). The authors advocate routine reporting of $\Delta_{\text{drivers}}$, co-design of driver representations, domain-informed regularizers, and rigorous personalization and robustness evaluations to generalize beyond glucose forecasting to other time series with sparse exogenous interventions.

Abstract

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.

The Driver-Blindness Phenomenon: Why Deep Sequence Models Default to Autocorrelation in Blood Glucose Forecasting

TL;DR

The paper addresses Driver-Blindness in blood glucose forecasting, where deep sequence models underutilize informative drivers (insulin, meals, activity), yielding near zero. It introduces an arousal–conspecificity framework and documents three interrelated failure modes—architectural bias (C1), fidelity gaps (C2), and personalization gaps (C3)—supported by cross-method evidence across Gaussian processes, RNNs, CNNs, Transformers, and TFT variants. It reviews mitigation strategies such as physiological encoder representations (IOB/COB/RaG), causal regularization, and personalization, noting that gains are partial (roughly 5–20% RMSE reduction by 60 minutes, often smaller at 30 minutes). The authors advocate routine reporting of , co-design of driver representations, domain-informed regularizers, and rigorous personalization and robustness evaluations to generalize beyond glucose forecasting to other time series with sparse exogenous interventions.

Abstract

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 , the performance gain of multivariate models over matched univariate baselines. Across the literature, 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 to prevent driver-blind models from being considered state-of-the-art.

Paper Structure

This paper contains 7 sections, 2 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Why Models Collapse into Deep Autocorrelation
  3. Arousal, internal dynamics, and external evidence
  4. Challenge C1: Architectural bias and representation limits
  5. Challenge C2: Fidelity gap and behavioral confounding
  6. Challenge C3: Personalization gap and non-stationarity
  7. Discussion: Implications, partial solutions, and recommendations

Figures (1)

  • Figure 1: Arousal–conspecificity framework. The horizontal axis ($\alpha$) controls the balance between internal dynamics (Prior/autocorrelation) and external evidence (Likelihood/drivers). The vertical axis represents conspecificity of driver signals. The desired state is a driver-engaged regime with high arousal and high conspecificity. However, Challenge C1 (architectural shortcuts, blue) pushes models toward low $\alpha$, while C2 (fidelity gaps, red) reduces perceived driver reliability. Training trajectories (purple dashed) therefore tend to collapse into a driver-blind regime where forecasts rely almost exclusively on autocorrelation.