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RESEARCH ON OPTIMIZATION OF HEAVY LOAD RAILWAY RAIL GRINDING PROFILE BASED ON ABRASION CONTROL
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Prediction of Acute Kidney Injury for Critically Ill Cardiogenic Shock Patients with Machine Learning Algorithms
Published 2025-01-01“…Xiaofei Zhang,1,* Yonghong Xiong,2,* Huilan Liu,3 Qian Liu,4 Shubin Chen5 1Department of Gerontology, China Aerospace Science & Industry Corporation 731 hospital, Beijing, People’s Republic of China; 2Department of Cardiology, Beijing Feng Tai Hospital, Beijing, People’s Republic of China; 3Department of Nephrology, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China; 4Department of Cardiology, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, People’s Republic of China; 5Department of Intensive Care Unit, China Aerospace Science & Industry Corporation 731 hospital, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qian Liu, Department of Cardiology, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science & Technology, No. 100 of Xianggang Road, Jiangan District, Wuhan, 430015, People’s Republic of China, Tel +86027-82433350, Email qian_liu1124@126.com Shubin Chen, Department of Intensive Care Unit, China Aerospace Science & Industry Corporation 731 hospital, No. 3 Zhen Gang Nan Li, Yun Gang Town, Feng Tai District, Beijing, 100074, People’s Republic of China, Tel +86010-68374065, Email 18610074016@163.comBackground: The aim of this study was to use five machine learning approaches and logistic regression to design and validate the acute kidney injury (AKI) prediction model for critically ill individuals with cardiogenic shock (CS).Methods: All patients who diagnosed with CS from the MIMIC-IV database, the eICU database, and Zhongnan hospital of Wuhan university were included in this study. …”
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