  Automated medical prognosis has gained interest as artificial intelligence
evolves and the potential for computer-aided medicine becomes evident.
Nevertheless, it is challenging to design an effective system that, given a
patient's medical history, is able to predict probable future conditions.
Previous works, mostly carried out over private datasets, have tackled the
problem by using artificial neural network architectures that cannot deal with
low-cardinality datasets, or by means of non-generalizable inference
approaches. We introduce a Deep Learning architecture whose design results from
an intensive experimental process. The final architecture is based on two
parallel Minimal Gated Recurrent Unit networks working in bi-directional
manner, which was extensively tested with the open-access Mimic-III dataset.
Our results demonstrate significant improvements in automated medical
prognosis, as measured with Recall@k. We summarize our experience as a set of
relevant insights for the design of Deep Learning architectures. Our work
improves the performance of computer-aided medicine and can serve as a guide in
designing artificial neural networks used in prediction tasks.
