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

Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (659) Nephrology/Renal Disorders (85) Machine Learning (1819) Intensive Care Unit (ICU) (143) Clinical Information and Decision Making (1721)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62932, first published .
Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study

Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study

Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study

Authors of this article:

Wei Jiang1 Author Orcid Image ;   Yaosheng Zhang2 Author Orcid Image ;   Jiayi Weng3 Author Orcid Image ;   Lin Song1 Author Orcid Image ;   Siqi Liu3 Author Orcid Image ;   Xianghui Li1 Author Orcid Image ;   Shiqi Xu1 Author Orcid Image ;   Keran Shi1 Author Orcid Image ;   Luanluan Li1 Author Orcid Image ;   Chuanqing Zhang1 Author Orcid Image ;   Jing Wang1 Author Orcid Image ;   Quan Yuan2 Author Orcid Image ;   Yongwei Zhang2 Author Orcid Image ;   Jun Shao1 Author Orcid Image ;   Jiangquan Yu1 Author Orcid Image ;   Ruiqiang Zheng1 Author Orcid Image
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Journals

  1. Zhou R, Wang H, Yang Q, Han L. Identification of the risk factors for predicting severe acute kidney injury in patients after liver transplantation. Frontiers in Physiology 2025;16 View