从预测到实践：一种面向任务的血糖预测评估框架
From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting
AI总结：
本文提出一种面向任务的血糖预测评估框架，通过评估低血糖预警和胰岛素剂量决策支持，揭示预测精度与任务相关实用性之间的差距。
摘要：
临床时间序列预测日益受到决策支持的关注，但标准聚合指标可能掩盖模型在实际任务中的有效性。在安全关键设置中，低平均误差可能与高风险区域的危险失败共存。本文提出一种面向任务的血糖预测评估框架，围绕两个下游应用：低血糖早期预警和胰岛素剂量决策支持。对于早期预警，我们使用真实数据和三个临床队列进行评估，采用事件级召回率和每患者日假警数，这些指标反映运营警报负担而非聚合准确性。我们发现，整体表现良好的模型在餐后切片中可能表现糟糕，因为胰岛素剂量升高，遗漏预警的临床后果最大。然而，标准预测评估不测试模型是否能推断行动效果，这对支持胰岛素剂量决策至关重要。因此，我们添加了第二条干预性手臂，使用FDA认可的UVA/Padova模拟器，评估预测者是否能预测改变胰岛素计划后的葡萄糖反应。我们发现，在真实数据预测中表现良好的模型在预测干预效果的方向、幅度或排名以及在临床动机成本下选择不良胰岛素剂量时表现不佳。两项手臂揭示了预测精度与任务相关实用性之间的持续差距。我们发布了基准、标准化预处理流程和模拟器基于的干预数据集作为可重复的工具包。
英文摘要：
Clinical time-series forecasting is increasingly studied for decision support, yet standard aggregate metrics can obscure whether a model is actually useful for the task it is meant to serve. In safety-critical settings, low average error can coexist with dangerous failures in exactly the high-risk regimes that matter most. We present a task-aware evaluation framework for blood glucose forecasting built around two downstream uses: hypoglycemia early warning and insulin dosing decision support. For early warning, we evaluate on real data from three clinical cohorts using event-level recall and false alarms per patient-day, metrics that reflect operational alarm burden rather than aggregate accuracy. We show that models appearing acceptable overall, with recall above 0.9 on the full test set, can fail badly in the post-bolus slice, where insulin-on-board is elevated and missed warnings carry the greatest clinical consequences. Standard forecasting evaluation, however, does not test whether a model can reason about the effects of actions, a requirement for supporting insulin dosing decisions. We therefore add a second, interventional arm using the FDA-accepted UVA/Padova simulator, where we evaluate whether forecasters can predict glucose responses to altered insulin plans in paired factual/counterfactual scenarios. We show that models that look strong on real-data forecasting often fail to predict the direction, magnitude, or ranking of intervention effects, and choose poor insulin doses when evaluated under a clinically motivated cost. Taken together, the two arms reveal a consistent gap between forecasting accuracy and task-relevant usefulness. We release the benchmark, the standardized preprocessing pipeline for public cohorts, and the simulator-based interventional dataset as a reproducible toolkit.
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