Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach.

Abstract

Authors

• Siqi Jiang
  Department of Computer Science, New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, United States.
• Lingyu Xu
  School of Computer Engineering and Science, Shanghai University, Shanghai, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
• Xinyuan Wang
  Proteomics and Metabolomics Core Facilities, West China Hospital, Sichuan University, Chengdu 610041, China.
• Chenyu Li
  Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi'an 710069, China.
• Chen Guan
  Department of Nephrology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, China.
• Lin Che
  Department of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, China.
• Yanfei Wang
  Department of Infectious Diseases, College of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
• Xuefei Shen
  Department of Nephrology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, China.
• Yan Xu
  Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.

Keywords

External Resources