  Automatically associating ICD codes with electronic health data is a
well-known NLP task in medical research. NLP has evolved significantly in
recent years with the emergence of pre-trained language models based on
Transformers architecture, mainly in the English language. This paper adapts
these models to automatically associate the ICD codes. Several neural network
architectures have been experimented with to address the challenges of dealing
with a large set of both input tokens and labels to be guessed. In this paper,
we propose a model that combines the latest advances in NLP and multi-label
classification for ICD-10 code association. Fair experiments on a Clinical
dataset in the French language show that our approach increases the $F_1$-score
metric by more than 55\% compared to state-of-the-art results.

Computation and Language;Artificial Intelligence