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Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models

Crossref DOI link: https://doi.org/10.1186/s12911-024-02793-9

Published Online: 2024-12-05

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Zhang, Xingyu

Wang, Yanshan

Jiang, Yun

Pacella, Charissa B.

Zhang, Wenbin
Funding

Funding for this research was provided by:

University of Pittsburgh Department of Communication Science and Disorders (Department internal fund)
License Information

Text and Data Mining valid from 2024-12-05

Version of Record valid from 2024-12-05
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Article History

Received: 18 August 2024

Accepted: 28 November 2024

First Online: 5 December 2024

Declarations

:

: Not applicable, as this study utilized publicly available, anonymized data from the NHAMCS-ED dataset.Not applicable, as the research was conducted using publicly available, anonymized data from the NHAMCS-ED dataset.

: Not applicable, as this study utilized publicly available, anonymized data.

: The authors declare no competing interests.

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