Multilingual Information Extraction in Clinical Texts Using Deep Learning Approaches

@Article {
title = {Multilingual Information Extraction in Clinical Texts Using Deep Learning Approaches},
pages = {111-119},
keywds = {
Biomedical NLP; Clinical Coding; Entity Linking; Entity Normalisation; Named Entity Recognition
}
abstract = {

This article briefly describes doctoral thesis research in biomedical natural language processing. The main goal of the research is to contribute to the task of automatic information extraction from unstructured clinical narratives, which includes entity (term, concept) detection and classification, entity linking and clinical coding. Amongst the contributions already made are: a tool for clinical codes mapping and interoperability ClinIDMap, participation in shared tasks dedicated to the clinical named entity recognition, linking and clinical coding where the results of the experiments were published. In addition, the data augmentation method with codes mapping is described.

}
date = {2023-09-28},
}