The authors propose two interpretable deep learning architectures, TA-RNN and TA-RNN-AE, that leverage time embedding and dual-level attention mechanisms to predict clinical outcomes in electronic health records at the next visit and multiple visits ahead, respectively.
SWoTTeD, a novel tensor decomposition method, extracts temporal phenotypes that accurately reconstruct and represent the complex temporal patterns in electronic health records.