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

Clinical Information and Decision Making (1935) Machine Learning (2095) Decision Support for Health Professionals (1555) Applications of AI (295) Artificial Intelligence (2162) Secondary Use of Clinical Data for Research and Surveillance (475) Gastroenterology and Hepatology (132)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66733, first published .
Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study

Authors of this article:

Jingchao Lei1 Author Orcid Image ;   Jia Zhai1 Author Orcid Image ;   Yao Zhang1 Author Orcid Image ;   Jing Qi1 Author Orcid Image ;   Chuanzheng Sun1 Author Orcid Image
Article Authors Cited by (6) Tweetations Metrics

Jingchao Lei   1 , PhD, MD ;   Jia Zhai   1 , PhD ;   Yao Zhang   1 , PhD ;   Jing Qi   1 * , PhD, MD ;   Chuanzheng Sun   1 * , Prof Dr, MD

1 Third Xiangya Hospital of Central South University, Changsha, China

*these authors contributed equally

Corresponding Author: