AI RESEARCH
From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting
arXiv CS.LG
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ArXi:2605.00645v1 Announce Type: new Clinical time-series forecasting is increasingly studied for decision, 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.