Named Entity Recognition in Prehospital Trauma Care

Natural language processing (NLP) methods would improve outcomes in the area of prehospital Emergency Medical Services (EMS) data collection and abstraction. This study evaluated off-the-shelf solutions for automating labelling of clinically relevant data from EMS reports. A qualitative approach for...

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Autores principales: Silverman, Greg M., Lindemann, Elizabeth A., Rajamani, Geetanjali, Finzel, Raymond L., McEwan, Reed, Knoll, Benjamin C., Pakhomov, Serguei, Melton, Genevieve B., Tignanelli, Christopher J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360018/
https://www.ncbi.nlm.nih.gov/pubmed/31438244
http://dx.doi.org/10.3233/SHTI190547
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author Silverman, Greg M.
Lindemann, Elizabeth A.
Rajamani, Geetanjali
Finzel, Raymond L.
McEwan, Reed
Knoll, Benjamin C.
Pakhomov, Serguei
Melton, Genevieve B.
Tignanelli, Christopher J.
author_facet Silverman, Greg M.
Lindemann, Elizabeth A.
Rajamani, Geetanjali
Finzel, Raymond L.
McEwan, Reed
Knoll, Benjamin C.
Pakhomov, Serguei
Melton, Genevieve B.
Tignanelli, Christopher J.
author_sort Silverman, Greg M.
collection PubMed
description Natural language processing (NLP) methods would improve outcomes in the area of prehospital Emergency Medical Services (EMS) data collection and abstraction. This study evaluated off-the-shelf solutions for automating labelling of clinically relevant data from EMS reports. A qualitative approach for choosing the best possible ensemble of pretrained NLP systems was developed and validated along with a feature using word embeddings to test phrase synonymy. The ensemble showed increased performance over individual systems.
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spelling pubmed-73600182020-07-14 Named Entity Recognition in Prehospital Trauma Care Silverman, Greg M. Lindemann, Elizabeth A. Rajamani, Geetanjali Finzel, Raymond L. McEwan, Reed Knoll, Benjamin C. Pakhomov, Serguei Melton, Genevieve B. Tignanelli, Christopher J. Stud Health Technol Inform Article Natural language processing (NLP) methods would improve outcomes in the area of prehospital Emergency Medical Services (EMS) data collection and abstraction. This study evaluated off-the-shelf solutions for automating labelling of clinically relevant data from EMS reports. A qualitative approach for choosing the best possible ensemble of pretrained NLP systems was developed and validated along with a feature using word embeddings to test phrase synonymy. The ensemble showed increased performance over individual systems. 2019-08-21 /pmc/articles/PMC7360018/ /pubmed/31438244 http://dx.doi.org/10.3233/SHTI190547 Text en http://creativecommons.org/licenses/by-nc/4.0/ This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
spellingShingle Article
Silverman, Greg M.
Lindemann, Elizabeth A.
Rajamani, Geetanjali
Finzel, Raymond L.
McEwan, Reed
Knoll, Benjamin C.
Pakhomov, Serguei
Melton, Genevieve B.
Tignanelli, Christopher J.
Named Entity Recognition in Prehospital Trauma Care
title Named Entity Recognition in Prehospital Trauma Care
title_full Named Entity Recognition in Prehospital Trauma Care
title_fullStr Named Entity Recognition in Prehospital Trauma Care
title_full_unstemmed Named Entity Recognition in Prehospital Trauma Care
title_short Named Entity Recognition in Prehospital Trauma Care
title_sort named entity recognition in prehospital trauma care
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360018/
https://www.ncbi.nlm.nih.gov/pubmed/31438244
http://dx.doi.org/10.3233/SHTI190547
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