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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Detalles Bibliográficos
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
Descripción
Sumario: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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