2019
EMNLP
EMNLP 2019
Using Snomed to recognize and index chemical and drug mentions.
Abstract
AbstractIn this paper we describe a new named entity extraction system. Our work proposes a system for the identification and annotation of drug names in Spanish biomedical texts based on machine learning and deep learning models. Subsequently, a standardized code using Snomed is assigned to these drugs, for this purpose, Natural Language Processing tools and techniques have been used, and a dictionary of different sources of information has been built. The results are promising, we obtain 78% in F1 score on the first sub-track and in the second task we map with Snomed correctly 72% of the found entities.
🌉
Interdisciplinary Bridge
- Artificial Intelligence and Healthcare & Medicine and Natural Language Processing
🐣
Hot Topic Early Bird
- entity extraction
Topics
Machine Learning > Core Methods > Classification
Deep Learning > Architectures > Neural Networks
Natural Language Processing > Understanding > Named Entity Recognition
Healthcare & Medicine > Research > Bioinformatics
Machine Learning > Learning Types > Deep Learning
Healthcare & Medicine > Clinical > Medical NLP