Journal of Medical Internet Research

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

Wei Jiang, Yaosheng Zhang, Jiayi Weng, Lin Song, Siqi Liu, Xianghui Li, Shiqi Xu, Keran Shi, Luanluan Li, Chuanqing Zhang, Jing Wang, Quan Yuan, Yongwei Zhang, Jun Shao, Jiangquan Yu, Ruiqiang Zheng

J Med Internet Res 2025;27:e62932

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Identification and Validation of an Explainable Prediction Model of Sepsis in Patients With Intracerebral Hemorrhage: Multicenter Retrospective Study

Xianglin Liu, Zhihua Huang, Yizhi Guo, Yandeng Li, Jianming Zhu, Jun Wen, Yunchun Gao, Jianyi Liu

J Med Internet Res 2025;27:e71413

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Designing Health Care Provider–Centered Emergency Department Interventions: Participatory Design Study

Woosuk Seo, Jiaqi Li, Zhan Zhang, Chuxuan Zheng, Hardeep Singh, Kalyan Pasupathy, Prashant Mahajan, Sun Young Park

JMIR Form Res 2025;9:e68891

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Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis

Ji Soo Park, Sa-Yoon Park, Jae Won Moon, Kwangsoo Kim, Dong In Suh

J Med Internet Res 2025;27:e66491

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A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

Model 2 included 11 variables that were used in the classification system developed by Park et al [9], including age, sex, emergency operation, operation duration, diabetes, ACEi or ARB usage, blood levels of albumin, hemoglobin, sodium, e GFR, and urine dipstick protein. In this model, light GBM (AUC=0.81) and DNN (AUC=0.8) showed the highest performance.

Ji Won Min, Jae-Hong Min, Se-Hyun Chang, Byung Ha Chung, Eun Sil Koh, Young Soo Kim, Hyung Wook Kim, Tae Hyun Ban, Seok Joon Shin, In Young Choi, Hye Eun Yoon