Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports

IMPORTANCE: Clinical text reports from head computed tomography (CT) represent rich, incompletely utilized information regarding acute brain injuries and neurologic outcomes. CT reports are unstructured; thus, extracting information at scale requires automated natural language processing (NLP). Howe...

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Detalles Bibliográficos
Autores principales: Torres-Lopez, Victor M., Rovenolt, Grace E., Olcese, Angelo J., Garcia, Gabriella E., Chacko, Sarah M., Robinson, Amber, Gaiser, Edward, Acosta, Julian, Herman, Alison L., Kuohn, Lindsey R., Leary, Megan, Soto, Alexandria L., Zhang, Qiang, Fatima, Safoora, Falcone, Guido J., Payabvash, M. Seyedmehdi, Sharma, Richa, Struck, Aaron F., Sheth, Kevin N., Westover, M. Brandon, Kim, Jennifer A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2022
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9382443/
https://www.ncbi.nlm.nih.gov/pubmed/35972739
http://dx.doi.org/10.1001/jamanetworkopen.2022.27109

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9382443/
https://www.ncbi.nlm.nih.gov/pubmed/35972739
http://dx.doi.org/10.1001/jamanetworkopen.2022.27109

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