Named entity recognition in prehospital trauma care

Greg M. Silverman, Elizabeth A. Lindemann, Geetanjali Rajamani, Raymond L. Finzel, Reed McEwan, Benjamin C. Knoll, Serguei Pakhomov, Genevieve B. Melton, Christopher J. Tignanelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Number of pages2
ISBN (Electronic)9781643680026
StatePublished - Aug 21 2019
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in health technology and informatics
ISSN (Print)0926-9630


Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019

Bibliographical note

Funding Information:
NIH NCATS UL1TR002494 and U01TR002062, NIGMS R01GM120079, and AHRQ R01HS022085 and R01HS024532.

Publisher Copyright:
© 2019 International Medical Informatics Association (IMIA) and IOS Press. 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).


  • Emergency medical services
  • Labeling
  • Natural language processing


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