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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Detalhes bibliográficos
Publicado no:PLoS One
Main Authors: 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: Artigo
Idioma:Inglês
Publicado em: Public Library of Science 2020
Assuntos:
Research Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7392233/
https://ncbi.nlm.nih.gov/pubmed/32730362
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0236827
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