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

Artificial Intelligence (4460) Clinical Information and Decision Making (3459) Decision Support for Health Professionals (2075) Machine Learning (2996) Applications of AI (808) Nephrology/Renal Disorders (131)

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 (4) Tweetations (2) Metrics

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