Yinghao Wang
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Development and validation of an interpretable machine learning model for predicting medium-to-giant coronary aneurysms in Kawasaki disease
Abstract Purpose: This study aimed to develop and validate an interpretable machine learning (ML) model using routinely collected clinical data to predict medium-to-giant coronary artery aneurysms (MGCAA) early in Kawasaki disease (KD). Methods: This retrospective study included 2,777 KD patients from two centers in China. Eleven ML algorithms were developed using clinical and laboratory data from electronic medical records (EMRs).
Molecular characterization of Ebola virus glycoprotein V75A substitution in the 2018-2020 epidemic
ArticleOnline nowOpen access 1School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China 2Guangzhou Key Laboratory of Clinical Pathogen Research for Emerging and Major Infectious Diseases, Institute of Infectious Diseases, Guangzhou Eighth People’s Hospital, Guangzhou Medical University, Guangzhou 510440, China 3Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510030, China 4Department of Infectious Diseases, Center of Infectious...
Prediction of Impact Stress Concentration in Octagonal Hollow 3D‐Woven CFRP: An ISCF‐Coupled Physical Model for Multi‐Scale Synergistic Weaving
Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References 1, “Advanced Composite Materials and Aerospace Engineering,” Acta Materiae Compositae Sinica 24, no. 1 (2007): 1–12. 2, “Overview of Modern Textile Composites,” Progress in Textile Science and Technology 4, no. 16 (2020): 1–18.
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