  Unstructured textual data are at the heart of health systems: liaison letters
between doctors, operating reports, coding of procedures according to the
ICD-10 standard, etc. The details included in these documents make it possible
to get to know the patient better, to better manage him or her, to better study
the pathologies, to accurately remunerate the associated medical acts\ldots All
this seems to be (at least partially) within reach of today by artificial
intelligence techniques. However, for obvious reasons of privacy protection,
the designers of these AIs do not have the legal right to access these
documents as long as they contain identifying data. De-identifying these
documents, i.e. detecting and deleting all identifying information present in
them, is a legally necessary step for sharing this data between two
complementary worlds. Over the last decade, several proposals have been made to
de-identify documents, mainly in English. While the detection scores are often
high, the substitution methods are often not very robust to attack. In French,
very few methods are based on arbitrary detection and/or substitution rules. In
this paper, we propose a new comprehensive de-identification method dedicated
to French-language medical documents. Both the approach for the detection of
identifying elements (based on deep learning) and their substitution (based on
differential privacy) are based on the most proven existing approaches. The
result is an approach that effectively protects the privacy of the patients at
the heart of these medical documents. The whole approach has been evaluated on
a French language medical dataset of a French public hospital and the results
are very encouraging.
