Temporal Cross-Attention for Dynamic Embedding and Tokenization of Multimodal Electronic Health Records

arXiv:2403.04012v1 Announce Type: new
Abstract: The breadth, scale, and temporal granularity of modern electronic health records (EHR) systems offers great potential for estimating personalized and contextual patient health trajectories using sequential deep learning. However, learning useful representations of EHR data is challenging due to its high dimensionality, sparsity, multimodality, irregular and variable-specific recording frequency, and timestamp duplication when multiple measurements are recorded simultaneously. Although recent efforts to fuse structured EHR and unstructured clinical notes suggest the potential for more accurate …

abstract arxiv attention cs.lg data deep learning dimensionality dynamic ehr electronic electronic health records embedding health however modern multimodal patient personalized records scale sparsity systems temporal tokenization type