Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study

BACKGROUND: Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography fo...

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Detalles Bibliográficos
Autores principales: Baskaran, Lohendran, Ying, Xiaohan, Xu, Zhuoran, Al’Aref, Subhi J., Lee, Benjamin C., Lee, Sang-Eun, Danad, Ibrahim, Park, Hyung-Bok, Bathina, Ravi, Baggiano, Andrea, Beltrama, Virginia, Cerci, Rodrigo, Choi, Eui-Young, Choi, Jung-Hyun, Choi, So-Yeon, Cole, Jason, Doh, Joon-Hyung, Ha, Sang-Jin, Her, Ae-Young, Kepka, Cezary, Kim, Jang-Young, Kim, Jin-Won, Kim, Sang-Wook, Kim, Woong, Lu, Yao, Kumar, Amit, Heo, Ran, Lee, Ji Hyun, Sung, Ji-min, Valeti, Uma, Andreini, Daniele, Pontone, Gianluca, Han, Donghee, Villines, Todd C., Lin, Fay, Chang, Hyuk-Jae, Min, James K., Shaw, Leslee 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/PMC7316297/
https://www.ncbi.nlm.nih.gov/pubmed/32584909
http://dx.doi.org/10.1371/journal.pone.0233791

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7316297/
https://www.ncbi.nlm.nih.gov/pubmed/32584909
http://dx.doi.org/10.1371/journal.pone.0233791

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