Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

Li, Xunliang; Wang, Peng; Zhu, Yuke; Zhao, Wenman; Pan, Haifeng; Wang, Deguang

Li, Xunliang; Wang, Peng; Zhu, Yuke; Zhao, Wenman; Pan, Haifeng; Wang, Deguang.

Affiliation

Li X; Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Wang P; Teaching Center for Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, China.
Zhu Y; Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Zhao W; Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Pan H; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.
Wang D; Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China. wangdeguang@ahmu.edu.cn.

BMC Med Inform Decis Mak ; 24(1): 148, 2024 May 31.

Article in En | MEDLINE | ID: mdl-38822285

Subject(s)

Acute Kidney Injury; Critical Illness; Machine Learning; Humans; Acute Kidney Injury/diagnosis; Middle Aged; Male; Female; Aged; Intensive Care Units; Adult

Key words

Acute kidney injury; Intensive care unit; Interpretability; Machine learning; Prediction model

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Critical Illness / Acute Kidney Injury / Machine Learning Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: En Journal: BMC Med Inform Decis Mak Journal subject: INFORMATICA MEDICA Year: 2024 Type: Article Affiliation country: China

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