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

Artificial Intelligence (2164) Nephrology/Renal Disorders (99) Clinical Information and Decision Making (1936) Decision Support for Health Professionals (1555) Machine Learning (2096) Applications of AI (295)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73840, first published .
Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study

Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study

Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study

Authors of this article:

Xinbo Ge1, 2 Author Orcid Image ;   Weiwei Chen1, 3 Author Orcid Image ;   Jianshan Shi4 Author Orcid Image ;   Jiaqiang Zhang5 Author Orcid Image ;   Hao Tai2 Author Orcid Image ;   Ying Zhang2 Author Orcid Image ;   Biao Wang2 Author Orcid Image ;   Wei Liu3 Author Orcid Image ;   Song Chen2, 6 Author Orcid Image ;   Huirui Han3 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics
All Time | Last Month | Last Six Months | Last Year
There are currently no tweets available for this article within the specified range.