针对医疗多传感器数据缺失，提出按特征和缺失时长定制化填补方案。
A Proposed Paradigm for Imputing Missing Multi-Sensor Data in the Healthcare Domain
- 根据特征性质和缺失时长选择不同填补方法
- 验证多种机器学习方法在长时间缺失场景下的有效性
- 适合需要精准血糖事件预测的可穿戴健康监测研究
糖尿病等慢性病管理面临重大挑战，尤其因低血糖等并发症需及时检测与干预。可穿戴传感器持续监测为早期预测血糖事件提供可能，但信号噪声和频繁缺失值限制了多传感器数据的有效利用。本研究分析现有数据集局限性，强调与低血糖预测相关的关键特征的时间特性。系统评估了当前先进研究中采用的填补技术，并考察了其他医疗领域机器学习与深度学习方法在处理长时间序列数据缺失方面的潜力。基于此，提出一种系统性范式：根据特定特征性质和缺失间隔时长，定制化选择填补策略。研究强调需深入探究各特征的时间动态，并实施多种特征专用填补方法，以有效应对数据中固有的异质时间模式。
原文摘要 · Abstract (English)
Chronic diseases such as diabetes pose significant management challenges, particularly due to the risk of complications like hypoglycemia, which require timely detection and intervention. Continuous health monitoring through wearable sensors offers a promising solution for early prediction of glycemic events. However, effective use of multisensor data is hindered by issues such as signal noise and frequent missing values. This study examines the limitations of existing datasets and emphasizes the temporal characteristics of key features relevant to hypoglycemia prediction. A comprehensive analysis of imputation techniques is conducted, focusing on those employed in state-of-the-art studies. Furthermore, imputation methods derived from machine learning and deep learning applications in other healthcare contexts are evaluated for their potential to address longer gaps in time-series data. Based on this analysis, a systematic paradigm is proposed, wherein imputation strategies are tailored to the nature of specific features and the duration of missing intervals. The review concludes by emphasizing the importance of investigating the temporal dynamics of individual features and the implementation of multiple, feature-specific imputation techniques to effectively address heterogeneous temporal patterns inherent in the data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。