忽视数据填补会严重影响心理健康研究结果，优化填补方法可提升预测准确率31%。
Imputation Matters: A Deeper Look into an Overlooked Step in Longitudinal Health and Behavior Sensing Research
- 提出针对纵向传感数据的改进填补策略
- 在抑郁预测任务中使AUROC提升31%
- 提醒研究者重视填补环节对结果的影响
长期被动传感研究在健康与行为分析中常面临数据缺失问题。我们对相关研究人员的初步访谈发现，多数人将数据填补视为低优先级步骤，常采用简单通用的填补方法而未评估其对研究结果的影响。本文通过公开可用的抑郁检测被动传感数据集，揭示了填补策略的重要性：优化后的填补方法使抑郁预测的AUROC相比原方法最高提升31%。研究最后讨论了纵向传感研究中有效填补面临的挑战与机遇。
原文摘要 · Abstract (English)
Longitudinal passive sensing studies for health and behavior outcomes often have missing and incomplete data. Handling missing data effectively is thus a critical data processing and modeling step. Our formative interviews with researchers working in longitudinal health and behavior passive sensing revealed a recurring theme: most researchers consider imputation a low-priority step in their analysis and inference pipeline, opting to use simple and off-the-shelf imputation strategies without comprehensively evaluating its impact on study outcomes. Through this paper, we call attention to the importance of imputation. Using publicly available passive sensing datasets for depression, we show that prioritizing imputation can significantly impact the study outcomes -- with our proposed imputation strategies resulting in up to 31% improvement in AUROC to predict depression over the original imputation strategy. We conclude by discussing the challenges and opportunities with effective imputation in longitudinal sensing studies.
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