  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.
