用软令牌缓解预测偏差，提升医疗时间序列的高风险场景可靠性。
Mitigating Exposure Bias in Risk-Aware Time Series Forecasting with Soft Tokens
- 用连续概率分布替代离散令牌，减少多步预测偏差
- 血糖预测风险降低18%，血压预测有效风险降约15%
- 适合糖尿病与血流动力学等安全敏感型控制场景
自回归预测在糖尿病和血流动力学管理的预测控制中至关重要，不同操作区间具有不同临床风险。标准模型采用教师强制训练会产生暴露偏差，导致闭环使用时多步预测不稳定。本文提出软令牌轨迹预测（SoTra），通过传播连续概率分布（‘软令牌’）来缓解暴露偏差，并学习校准的、带不确定性的轨迹。随后的风险感知解码模块最小化预期临床伤害。在血糖预测中，SoTra将平均分区风险降低18%；在血压预测中，有效临床风险降低约15%。这些改进支持其在高安全性要求的预测控制中的应用。
原文摘要 · Abstract (English)
Autoregressive forecasting is central to predictive control in diabetes and hemodynamic management, where different operating zones carry different clinical risks. Standard models trained with teacher forcing suffer from exposure bias, yielding unstable multi-step forecasts for closed-loop use. We introduce Soft-Token Trajectory Forecasting (SoTra), which propagates continuous probability distributions (``soft tokens'') to mitigate exposure bias and learn calibrated, uncertainty-aware trajectories. A risk-aware decoding module then minimizes expected clinical harm. In glucose forecasting, SoTra reduces average zone-based risk by 18\%; in blood-pressure forecasting, it lowers effective clinical risk by approximately 15\%. These improvements support its use in safety-critical predictive control.
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