Health AI Central
Peer-Reviewed Publication
Int Urol Nephrol2026September 1, 2026Journal Article

Machine learning models for early detection of urinary tract infections in kidney transplant patients.

Alejandro Camargo-Salamanca1, Liceth Viviana Castañeda-Silva2, Carlos Puentes-Morales3, Alejandro Duitama-Leal3, Andrés Cardona-Mendoza4, Alejandro Ramos-Casallas4, Sandra J Perdomo4, Consuelo Romero-Sánchez4, Julia Andrea Gomez-Montero5, Andrea Garcia-Lopez6, Fernando Girón-Luque5,7
1Nephrology Department, Colombiana de Trasplantes, Av. Carrera 30 # 47 A - 47., Bogotá DC, Colombia.
2Virtualization and Artificial Intelligence Advanced Solutions Laboratory (SavIA-Lab), Universidad El Bosque, Bogotá, Colombia.
3Virtualization and Artificial Intelligence Advanced Solutions Laboratory (SavIA-Lab), SIGNOS Group, Science Faculty, Universidad El Bosque, Bogotá, Colombia.
4Virtualization and Artificial Intelligence Advanced Solutions Laboratory (SavIA-Lab), Cellular and Molecular Immunology Group (INMUBO), Universidad El Bosque, Bogotá, Colombia.
5Research Department, Colombiana de Trasplantes, Bogotá, Colombia.
6Research Department, Colombiana de Trasplantes, Bogotá, Colombia. aegarcia@colombianadetrasplantes.com.
7Transplant Surgery, Colombiana de Trasplantes, Bogotá, Colombia.
PubMed DOI

Abstract

BACKGROUND: Renal transplantation is the preferred treatment for end-stage chronic kidney disease but requires lifelong immunosuppression, increasing the risk of infections such as urinary tract infection (UTI). UTI in kidney transplant recipients can lead to serious complications, including acute kidney injury, reduced graft survival, and increased mortality. Machine learning can enhance risk det…

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