In this work, we propose a novel clinical event time-series model based on the long short-term memory architecture (LSTM) that can predict future event occurrences for a large number of different clinical events. Our model relies on two sources of information to predict future events. One source is derived from the set of recently observed clinical events. The other one is based on the hidden state space defined by the LSTM that aims to abstract past, more distant, patient information that is predictive of future events. We evaluate our proposed model on electronic health record (EHRs) data derived from MIMIC-III dataset. We show that the combination of the two sources of information implemented in our method leads to improved prediction performance compared to the models based on individual sources.