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.
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Interdisciplinary Bridge
— Deep Learning and Healthcare & Medicine and Machine Learning and Natural Language Processing
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Keyword Pioneer
— drug mention
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Hot Topic Early Bird
— entity extraction
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Topics
Machine Learning > Core Methods > Classification
Deep Learning > Architectures > Neural Networks
Healthcare & Medicine > Research > Bioinformatics
Natural Language Processing > Applications > Named Entity Recognition
Machine Learning > Learning Types > Deep Learning
Healthcare & Medicine > Clinical > Medical NLP