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

Artificial Intelligence (4542) Clinical Information and Decision Making (3541) Decision Support for Health Professionals (2122) Machine Learning (3053) Applications of AI (863) Nephrology/Renal Disorders (134)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73840, first published .
Doctor interacting with futuristic display showing kidney health risk and data.

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 (6) Tweetations (2) Metrics

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  4. Wang C, Ye R, Gong X, Tang M, Ding F, Xie Y, Sheng Y, Nie X, He Y. Development and internal validation of a prediction model for early identification of sepsis-associated acute kidney injury based on admission serum biomarkers: a retrospective cohort study. Frontiers in Medicine 2026;13 View
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