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

Artificial Intelligence (4634) Clinical Information and Decision Making (3611) Decision Support for Health Professionals (2173) Machine Learning (3107) Applications of AI (894) 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 (9) 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
  5. Hu J, Shu B, Zhang D, Tan S, Sheng Y, Wu Y. Development and validation of an explainable machine learning model for predicting acute kidney injury in critically ill patients with primary peritonitis: a multicenter cohort study. Renal Failure 2026;48(1) View
  6. Amangeldy B, Boltaboyeva A, Baigarayeva Z, Imanbek B, Tasmurzayev N, Kurmanbek S, Tuleukhanov S, Lokshin V. Predicting Adverse ICU Outcomes from Admission-Time Frailty and Nocturnal Sleep Fragmentation Using Explainable AI. Journal of Clinical Medicine 2026;15(14):5538 View
  7. Ibraheem M, Khalil M, Khalil S, Contreras H, Azar J. Early clinical decision support using interpretable artificial intelligence in acute illness (sepsis and infection): A systematic review and meta-analysis. International Journal of Medical Informatics 2026;220:106629 View
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