Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing

BACKGROUND: Heart failure (HF) is a major cause of morbidity and mortality. However, much of the clinical data is unstructured in the form of radiology reports, while the process of data collection and curation is arduous and time-consuming. PURPOSE: We utilized a machine learning (ML)-based natural...

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Detalles Bibliográficos
Autores principales: Pandey, Mohit, Xu, Zhuoran, Sholle, Evan, Maliakal, Gabriel, Singh, Gurpreet, Fatima, Zahra, Larine, Daria, Lee, Benjamin C., Wang, Jing, van Rosendael, Alexander R., Baskaran, Lohendran, Shaw, Leslee J., Min, James K., Al’Aref, Subhi J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392233/
https://www.ncbi.nlm.nih.gov/pubmed/32730362
http://dx.doi.org/10.1371/journal.pone.0236827

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392233/
https://www.ncbi.nlm.nih.gov/pubmed/32730362
http://dx.doi.org/10.1371/journal.pone.0236827

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