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

Journals

  1. Gao B, Jin H, Zhang Y, Chen J. An Interpretable Machine Learning Model for Early Multitemporal Prediction of Onset of Acute Kidney Injury in Intensive Care Unit Patients with Severe Trauma. Journal of Intensive Care Medicine 2025 View
  2. Chen Y, Huang W, Zhao S, Ge Z, Liu Y, Huang R, Li D, Xu Q, Long X, Wei K, Chen Q, Sheng C, Tang C, Bai X. Predictive value of systemic inflammation response index for atherosclerotic cardiovascular disease risk in patients with hypercholesterolemia: a machine learning study with dual-cohort validation. Lipids in Health and Disease 2025;24(1) View
  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
  5. Hao D, Shao G, Wang X, Zhao C, Du J, Wang H, Ren Y, Song Y, Wen X. Development and validation of a prediction model for invasive syndrome in liver abscess patients based on LASSO regression: a multi-center retrospective cohort study in China. Frontiers in Medicine 2025;12 View
  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