Neural translation and automated recognition of ICD10 medical entities from natural language [article]

Louis Falissard, Claire Morgand, Sylvie Roussel, Claire Imbaud, Walid Ghosn, Karim Bounebache, Grégoire Rey
<span title="2020-05-06">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... plex 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:2004.13839v2</a> <a target="_blank" rel="external noopener" href="">fatcat:v3ff7kwp7vdtdhsdxma66gp6qa</a> </span>
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