  This article presents a novel method for predicting suicidal ideation from
Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data
using deep sequential models. Both EHR longitudinal data and EMA question forms
are defined by asynchronous, variable length, randomly-sampled data sequences.
In our method, we model each of them with a Recurrent Neural Network (RNN), and
both sequences are aligned by concatenating the hidden state of each of them
using temporal marks. Furthermore, we incorporate attention schemes to improve
performance in long sequences and time-independent pre-trained schemes to cope
with very short sequences. Using a database of 1023 patients, our experimental
results show that the addition of EMA records boosts the system recall to
predict the suicidal ideation diagnosis from 48.13% obtained exclusively from
EHR-based state-of-the-art methods to 67.78%. Additionally, our method provides
interpretability through the t-SNE representation of the latent space. Further,
the most relevant input features are identified and interpreted medically.
