医疗领域多传感器数据缺失值的一个 proposed 方法范式

糖尿病等慢性病构成了显著的管理挑战，尤其是由于易发生并发症如低血糖，需要及时检测和干预。通过可穿戴传感器进行持续健康监测 offers a promise 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。