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Title:

Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry.

Document type:
Article; Clinical Trial; Journal Article
Author(s):
Park, Hyung-Bok; Lee, Jina; Hong, Yongtaek; Byungchang, So; Kim, Wonse; Lee, Byoung K; Lin, Fay Y; Hadamitzky, Martin; Kim, Yong-Jin; Conte, Edoardo; Andreini, Daniele; Pontone, Gianluca; Budoff, Matthew J; Gottlieb, Ilan; Chun, Eun Ju; Cademartiri, Filippo; Maffei, Erica; Marques, Hugo; Gonçalves, Pedro de A; Leipsic, Jonathon A; Shin, Sanghoon; Choi, Jung H; Virmani, Renu; Samady, Habib; Chinnaiyan, Kavitha; Stone, Peter H; Berman, Daniel S; Narula, Jagat; Shaw, Leslee J; Bax, Jeroen J; Min, J...     »
Abstract:
BACKGROUND AND HYPOTHESIS: The recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner. METHODS: From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter st...     »
Journal title abbreviation:
Clin Cardiol
Year:
2023
Journal volume:
46
Journal issue:
3
Pages contribution:
320-327
Fulltext / DOI:
doi:10.1002/clc.23964
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/36691990
Print-ISSN:
0160-9289
TUM Institution:
Institut für Radiologie und Nuklearmedizin
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