区分不同评估者，用生理信号分析疼痛变化事件
Event-Aligned Analysis of Multi-Rater Pain Assessments Using Continuous Wearable Physiology
- 按评估者身份将稀疏疼痛评分转为离散事件，对齐可穿戴生理信号
- 发现患者、护士、医生间疼痛判断差异显著，且生理反应存在评估者依赖性
- 适合临床疼痛研究与可穿戴健康监测系统开发者参考
疼痛评估在患者、护士和临床医生之间存在差异，但多数计算方法假设单一真实标签，忽略了评估者的身份。本文提出一种考虑评估者身份的事件对齐框架，将稀疏的、特定于评估者的疼痛评分转化为离散的疼痛变化事件，并将连续可穿戴生理信号与这些事件对齐，全程保留评估者身份。该框架应用于脊柱相关疼痛手术期间采集的多模态可穿戴数据，揭示了评估者群体间的显著分歧，并初步提供了评估者依赖性生理差异的证据——在报告疼痛上升前已出现。结果表明，疼痛-生理关系可能并非评估者无关，跨评估者聚合评分可能掩盖有意义的生理模式。因此，考虑评估者身份的事件对齐视角，是解释真实临床场景中可穿戴数据的有前景方向。
原文摘要 · Abstract (English)
Pain is assessed differently by patients, nurses, and clinicians, yet most computational approaches assume a single ground-truth label - effectively ignoring who is doing the rating. We introduce a rater-aware, event-aligned framework that converts sparse, rater-specific pain ratings into discrete pain-change events and aligns continuous wearable physiological signals to these events, preserving rater identity throughout. Applied to multimodal wearable data collected during spine-related pain procedures, the framework identifies substantial disagreement across rater groups and provides preliminary, exploratory evidence of rater-dependent physiological differences preceding reported pain increases. These findings suggest that pain-physiology relationships may not be rater-invariant, and that aggregating assessments across raters may mask meaningful physiological patterns. A rater-aware, event-aligned perspective is therefore a promising direction for interpreting wearable data in real-world clinical pain assessment.
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