Journal article
Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives
Liliya Akhtyamova, Paloma Martinez, Karin Verspoor, John Cardiff
IEEE Access | Institute of Electrical and Electronics Engineers (IEEE) | Published : 2020
Abstract
In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute by extracting relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patients’ discharge summaries and radiology reports. The extracted information can help in health-related decision making processes. The Named Entity Recognition (NER) task, which detects important concepts in texts (e.g., diseases, symptoms, drugs, etc.), is crucial in the information extraction process yet has received little attention in languages other than English. In ..
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Awarded by Research Program of the Ministry of Economy and Competitiveness - Government of Spain, DeepEMR project
Funding Acknowledgements
This work was supported in part by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, DeepEMR project, under Grant, TIN2017-87548-C2-1-R. The work of Karin Verspoor was supported by the University of Melbourne, through a Study Leave grant. The work of Liliya Akhtyamova was supported by the Technological University Dublin as part of a President's Research Award, and a traineeship in UC3M Spain, through an Erasmus+ grant.