Fiche Logiciel - DNorm



DNorm is an automated method for determining which diseases are mentioned in biomedical text, the task of disease normalization. Diseases have a central role in many lines of biomedical research, making this task important for many lines of inquiry, including etiology (e.g. gene-disease relationships) and clinical aspects (e.g. diagnosis, prevention, and treatment). DNorm is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. DNorm is the first technique to use machine learning to normalize disease names and also the first method employing pairwise learning to rank in a normalization task. DNorm achieved the best performance in the 2013 ShARe/CLEF shared task on disease normalization in clinical notes.


Discipline(s): Apprentissage automatique
Domaine(s) d'application: Biomédicale
Mot(s)-clé(s): Non renseigné

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