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

Clinical Information and Decision Making (1936) Machine Learning (2096) Decision Support for Health Professionals (1555) Applications of AI (295) Artificial Intelligence (2165) Secondary Use of Clinical Data for Research and Surveillance (476) 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 (8) Tweetations Metrics

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  3. Ao T, Huang Y, Zhen P, Hu M. Association between serum phosphate levels and 28-day mortality in patients with sepsis-associated liver injury: a cohort study. BMC Infectious Diseases 2025;25(1) View
  4. Luo P, Li K, Xie Y, Huang K, Qin Z, Cai J, Fu Y, Cao J, Cao S, Zhou Z, Ye Z, Yuan S. Predictive modeling & mechanistic validation of synergistic pimodivir combinations for anti-influenza therapy via PB2cap affinity boost. npj Digital Medicine 2025;8(1) View
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  6. Chen Y, Zhang S, Chen S, Jiang S, Zhou S, Liu J, Liu Z, Lin R, Xu J. Concentration monitoring and dose optimization for infliximab in Crohn’s disease patients: a machine learning-based covariate ensemble model. Frontiers in Immunology 2025;16 View
  7. Nishibe T, Iwasa T, Toda N, Akiyama S, Kano M, Iwahori A, Fukuda S, Koizumi J, Nishibe M. Comparison of Different Machine Learning Methods in Prediction of Long-Term Survival After Endovascular Aneurysm Repair. Vascular and Endovascular Surgery 2025 View
  8. Guo J, Xiong F, Sun B, Lei M, Qin Y. Ensemble machine learning for early mortality risk stratification in septic orthopedic trauma: an international cohort study. International Journal of Medical Informatics 2025:106240 View