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