Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.

Ravindhran, Bharadhwaj; Chandak, Pankaj; Schafer, Nicole; Kundalia, Kaushal; Hwang, Woochan; Antoniadis, Savvas; Haroon, Usman; Zakri, Rhana Hassan

Ravindhran, Bharadhwaj; Chandak, Pankaj; Schafer, Nicole; Kundalia, Kaushal; Hwang, Woochan; Antoniadis, Savvas; Haroon, Usman; Zakri, Rhana Hassan.

Afiliación

Ravindhran B; Department of Renal Transplantation, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Chandak P; Department of Renal Transplantation, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Schafer N; Centre for Nephrology, Urology and Transplantation, King's College London, London, UK.
Kundalia K; Department of Renal Transplantation, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Hwang W; Department of Renal Transplantation, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Antoniadis S; Department of Renal Transplantation, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Haroon U; Department of Renal Transplantation, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Zakri RH; Department of Renal Transplantation, Guy's and St Thomas' NHS Foundation Trust, London, UK.

BJS Open ; 7(2)2023 03 07.

Article en En | MEDLINE | ID: mdl-36987687

Asunto(s)

Trasplante de Riñón; Insuficiencia Renal; Humanos; Trasplante de Riñón/efectos adversos; Supervivencia de Injerto; Sensibilidad y Especificidad; Curva ROC; Aprendizaje Automático

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trasplante de Riñón / Insuficiencia Renal Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: BJS Open Año: 2023 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Reino Unido

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