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Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care.
Wang, Lin; Duan, Shao-Bin; Yan, Ping; Luo, Xiao-Qin; Zhang, Ning-Ya.
Affiliation
  • Wang L; Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • Duan SB; Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • Yan P; Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • Luo XQ; Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • Zhang NY; Information Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Ren Fail ; 45(1): 2215329, 2023 Dec.
Article in En | MEDLINE | ID: mdl-37218683
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Full text: 1 Database: MEDLINE Main subject: Critical Care / Intensive Care Units Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Aged / Humans Language: En Journal: Ren Fail Journal subject: NEFROLOGIA Year: 2023 Type: Article Affiliation country: China
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Full text: 1 Database: MEDLINE Main subject: Critical Care / Intensive Care Units Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Aged / Humans Language: En Journal: Ren Fail Journal subject: NEFROLOGIA Year: 2023 Type: Article Affiliation country: China