Work

Named Entity Recognition in Prehospital Trauma Care

Year: 2019

Type: book-chapter

Abstract: 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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti190547

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

Institutions: University of Minnesota, Stanford University, North Memorial Medical Center, North Memorial Health Care

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: Agency for Healthcare Research and Quality, National Institute of General Medical Sciences, National Center for Advancing Translational Sciences

Awards: Innovative Methods for Real-time Risk Modeling of Postoperative Complications, G235943272, G287445649, DISCOVERY AND VISUALIZATION OF NEW INFORMATION FROM CLINICAL REPORTS IN THE EHR, G829367134

OpenAlex ID: W2990834835

FWCI: 2.033

Cites: 2

Cited by: 10

Related to: 10