Online interpretable dynamic prediction models for clinically significant posthepatectomy liver failure based on machine learning algorithms: a retrospective cohort study.

Abstract

Authors

• Yuzhan Jin
  School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
• Wanxia Li
  School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing.
• Yachen Wu
  Department of Hepatobiliary Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, People's Republic of China.
• Qian Wang
  Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China.
• Zhiqiang Xiang
  Department of Hepatobiliary Surgery, Hunan University of Medicine General Hospital, Huaihua, Hunan, China.
• Zhangtao Long
  The First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
• Hao Liang
  a Marine College Shandong University (weihai) , Shandong , China .
• Jianjun Zou
  School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
• Zhu Zhu
  Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou 310052, China.
• Xiaoming Dai
  The First Affiliated Hospital, Department of Hepatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China. Electronic address: fydaixiaoming@126.com.

Keywords

External Resources