  The recognition of medical entities from natural language is an ubiquitous
problem in the medical field, with applications ranging from medical act coding
to the analysis of electronic health data for public health. It is however a
complex task usually requiring human expert intervention, thus making it
expansive and time consuming. The recent advances in artificial intelligence,
specifically the raise of deep learning methods, has enabled computers to make
efficient decisions on a number of complex problems, with the notable example
of neural sequence models and their powerful applications in natural language
processing. They however require a considerable amount of data to learn from,
which is typically their main limiting factor. However, the C\'epiDc stores an
exhaustive database of death certificates at the French national scale,
amounting to several millions of natural language examples provided with their
associated human coded medical entities available to the machine learning
practitioner. This article investigates the applications of deep neural
sequence models to the medical entity recognition from natural language
problem.
