We introduce a novel classification framework for time-series imputation using deep learning, with a particular focus on clinical data. By identifying conceptual gaps in the literature and existing reviews, we devise a taxonomy grounded on the inductive bias of neural imputation frameworks, resulting in a classification of existing deep imputation strategies based on their suitability for specific imputation scenarios and data-specific properties. Our review further examines the existing methodologies employed to benchmark deep imputation models, evaluating their effectiveness in capturing the missingness scenarios found in clinical data and emphasising the importance of reconciling mathematical abstraction with clinical insights. Our classification aims to serve as a guide for researchers to facilitate the selection of appropriate deep learning imputation techniques tailored to their specific clinical data. Our novel perspective also highlights the significance of bridging the gap between computational methodologies and medical insights to achieve clinically sound imputation models.

翻译：本文提出了一种基于深度学习的时间序列填补新型分类框架，特别聚焦于临床数据。通过识别现有文献与综述中的概念空白，我们构建了基于神经填补框架归纳偏置的分类体系，从而依据不同填补场景和数据特性的适配性对现有深度填补策略进行分类。本综述进一步考察了当前用于基准测试深度填补模型的方法论，评估其在捕捉临床数据缺失场景方面的有效性，并强调协调数学抽象与临床认知的重要性。该分类体系旨在为研究人员提供指导，帮助其根据特定临床数据选择恰当的深度学习填补技术。我们的新视角同时凸显了弥合计算方法与医学认知之间鸿沟的重要性，以实现符合临床实际的填补模型。