论文标题
可穿戴感应研究中的地面真相权衡
The Ground Truth Trade-Off in Wearable Sensing Studies
论文作者
论文摘要
Perez等人的研究使用Apple Watch识别房颤（AF）是大规模机器学习的分水岭，用于可穿戴计算。确定相关患者对于医疗保健研究非常重要。对于像AF这样的条件，这可能会使中风风险降低三分之二。在Perez等人的研究中，只有42万个人中只有450个具有基础真相数据。他们的研究不使用不规则的脉冲通知，排除了417,000名参与者。该设计决策意味着他们的研究只能报告积极的预测价值（PPV），无法探索敏感性或特异性。在这篇社论中，我们探讨了获得地面真相数据及其对研究设计的影响的困难。
Perez et al's study using the Apple Watch to identify atrial fibrillation (AF) is a watershed moment in large-scale machine learning for wearable computing. Identifying relevant patients will be tremendously important to research in healthcare. For a condition like AF, this could reduce stroke risk by two thirds. In the study by Perez et al, only 450 out of 420,000 individuals had ground truth data. Their study excluded 417,000 participants using the irregular pulse notification. This design decision means their study was only able to report positive predictive value (PPV) and unable to explore sensitivity or specificity. In this editorial, we explore the difficulty of obtaining ground truth data and its implications for study design.
