This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2226) Clinical Informatics (919) Machine Learning (1401) Decision Support for Health Professionals (1147) Clinical Information and Decision Making (1340) Intensive Care Unit (ICU) (102)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42452, first published .
Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study

Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study

Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study

Authors of this article:

Jiang Li1 Author Orcid Image ;   Fengchan Xi2, 3 Author Orcid Image ;   Wenkui Yu3 Author Orcid Image ;   Chuanrui Sun1 Author Orcid Image ;   Xiling Wang1 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Jiang Li   1 * , BSc ;   Fengchan Xi   2, 3 * , MD ;   Wenkui Yu   3 * , MD ;   Chuanrui Sun   1 , BSc ;   Xiling Wang   1 , PhD

1 School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China

2 Research Institute of General Surgery, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China

3 Department of Intensive Care Unit, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China

*these authors contributed equally

Corresponding Author:

  • Xiling Wang, PhD
  • School of Public Health and Key Laboratory of Public Health Safety
  • Fudan University
  • No 130 Dongan Road
  • Xuhui District
  • Shanghai, 200032
  • China
  • Phone: 86 021-54237051
  • Email: erinwang@fudan.edu.cn