“…One of the most popular architectures is recurrent neural networks (RNNs), which make predictions according to the sequence of historical events. Dozens of successes have been achieved in applying deep learning to disease predictions [ 24 ], mostly using variants of RNNs with distinct network components, for example, by adding an attention mechanism to evaluate the weights of each variable [ 25 - 29 ] or by using special configurations to tackle the problem of time decays [ 23 , 25 , 27 , 30 - 32 ]. Typical prediction tasks include the prediction of diabetes mellitus [ 23 ], Parkinson disease [ 29 , 33 ], chronic heart failure [ 26 ], sepsis [ 34 ], mortality, and readmission [ 25 ].…”