Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study.

Abstract

Authors

• C Zhu
  From the School of Computer Science and Engineering (R.H., B.Z., C.Z.) anandawork@126.com.
• Z Xu
  Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
• Y Gu
  Department of Pathology, the Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
• S Zheng
  Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
• X Sun
  Department of Nursing, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
• J Cao
• B Song
  Department of Nursing, Henan Provincial People's Hospital, Zhengzhou, China.
• J Jin
  Department of Nursing, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
• Y Liu
  Google Health Palo Alto California USA.
• X Wen
  Department of Nursing, Sichuan Provincial People's Hospital, Chengdu, China.
• S Cheng
  Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
• J Li
  Department of Pulmonary and Critical Care Medicine, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
• X Wu
  Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark.

Keywords

External Resources