Predicting the risk of pulmonary infection after kidney transplantation using machine learning methods: a retrospective cohort study.

Journal: International urology and nephrology
PMID: 39488661

Abstract

PURPOSE: Pulmonary infection is the most common and serious complication after kidney transplantation that affects the survival of the transplanted kidney and the quality of life of patients. This study aims to construct a machine learning model for predicting the risk of pulmonary infection after kidney transplantation.

Authors

  • Xiaoting Wu
    Department of Cardiac Surgery, Michigan Medicine, Ann Arbor, Mich.
  • Hailing Zhang
    Department of Nursing, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China.
  • Minglong Cai
    Department of Rheumatology and Immunology, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China.
  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Anlan Xu
    Department of Gynecology, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China. 17318598159@163.com.

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