  The digitalization of stored information in hospitals now allows for the
exploitation of medical data in text format, as electronic health records
(EHRs), initially gathered for other purposes than epidemiology. Manual search
and analysis operations on such data become tedious. In recent years, the use
of natural language processing (NLP) tools was highlighted to automatize the
extraction of information contained in EHRs, structure it and perform
statistical analysis on this structured information. The main difficulties with
the existing approaches is the requirement of synonyms or ontology
dictionaries, that are mostly available in English only and do not include
local or custom notations. In this work, a team composed of oncologists as
domain experts and data scientists develop a custom NLP-based system to process
and structure textual clinical reports of patients suffering from breast
cancer. The tool relies on the combination of standard text mining techniques
and an advanced synonym detection method. It allows for a global analysis by
retrieval of indicators such as medical history, tumor characteristics,
therapeutic responses, recurrences and prognosis. The versatility of the method
allows to obtain easily new indicators, thus opening up the way for
retrospective studies with a substantial reduction of the amount of manual
work. With no need for biomedical annotators or pre-defined ontologies, this
language-agnostic method reached an good extraction accuracy for several
concepts of interest, according to a comparison with a manually structured
file, without requiring any existing corpus with local or new notations.
