Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease.

Zahr, Rima S; Mohammed, Akram; Naik, Surabhi; Faradji, Daniel; Ataga, Kenneth I; Lebensburger, Jeffrey; Davis, Robert L

Zahr, Rima S; Mohammed, Akram; Naik, Surabhi; Faradji, Daniel; Ataga, Kenneth I; Lebensburger, Jeffrey; Davis, Robert L.

Afiliação

Zahr RS; Division of Pediatric Nephrology and Hypertension, University of Tennessee Health Science Center Memphis, Memphis, Tennessee, USA.
Mohammed A; Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
Naik S; Department of Surgery, University of Tennessee Health Sciences Center, Memphis, Tennessee, USA.
Faradji D; College of Medicine, University of Tennessee Health Sciences Center, Memphis, Tennessee, USA.
Ataga KI; Center for Sickle Cell Disease, University of Tennessee Health Sciences Center, Memphis, Tennessee, USA.
Lebensburger J; Division of Pediatric Hematology and Oncology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Davis RL; Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.

Am J Nephrol ; 55(1): 18-24, 2024.

Article em En | MEDLINE | ID: mdl-37906980

Assuntos

Injúria Renal Aguda; Anemia Falciforme; Adulto; Humanos; Feminino; Masculino; Injúria Renal Aguda/diagnóstico; Injúria Renal Aguda/epidemiologia; Injúria Renal Aguda/etiologia; Anemia Falciforme/complicações; Anemia Falciforme/epidemiologia; Rim; Medição de Risco; Aprendizado de Máquina; Estudos Retrospectivos

Palavras-chave

Acute kidney injury; Machine learning; Predictive analytics; Sickle cell disease; Time-series data

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Injúria Renal Aguda / Anemia Falciforme Limite: Adult / Female / Humans / Male Idioma: En Ano de publicação: 2024 Tipo de documento: Article

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