AIMC Topic: Kidney Transplantation
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Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation.
Scientific reports
Aug 4, 2025
Novel methods for detecting transplant rejection are craved, since conventional methods can detect ongoing rejection that may sometimes have already caused irreversible damage in transplanted organs. Here, we applied a transcriptomics database of rec...
A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization.
BMC medical informatics and decision making
Jul 8, 2025
BACKGROUND: Anaemia is a common complication after kidney transplantation, and the haemoglobin concentration is one of the main criteria for identifying anaemia. Moreover, artificial intelligence methods have developed rapidly in recent years, are wi...
Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.
Renal failure
Jul 3, 2025
Kidney transplantation (KT) is an effective treatment for end-stage renal disease; however, the lifelong immunosuppressive regimen increases the risk of infection, presenting significant clinical, and economic challenges. Identifying predictive bioma...
Personalized prediction model generated with machine learning for kidney function one year after living kidney donation.
Scientific reports
Jul 1, 2025
Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies among individuals. This study aimed to develop a machine learning (ML) model to predict serum creatinin...
Exploring the relationship between graft dysfunction with serum metabolites and inflammatory proteins: integrating Mendelian randomization, single-cell analysis, machine learning, and SHAP methods for comprehensive analysis.
Renal failure
Jun 25, 2025
BACKGROUND: The exact mechanism of graft dysfunction has not been fully clarified. We aimed to explore the causal effects of serum metabolites on graft dysfunction and the mediating role of inflammatory proteins.
Comparison of Machine Learning Algorithms and Bayesian Estimation in Predicting Tacrolimus Concentration in Tunisian Kidney Transplant Patients During the Early Post-Transplant Period.
European journal of drug metabolism and pharmacokinetics
May 8, 2025
BACKGROUND AND OBJECTIVE: Model-informed precision dosing (MIPD), based on a Bayesian approach and machine learning (ML) algorithms, is a suitable approach to personalize dosage recommendations and to improve the concentration target attainment for e...
Implementing large language model and retrieval augmented generation to extract geographic locations of illicit transnational kidney trade.
International journal of health geographics
Apr 28, 2025
BACKGROUND: Illicit kidney trade networks, operating globally, involve intricate interactions among various players, most notably buyers, sellers, brokers, and surgeons. A comprehensive understanding of these trade networks is, however, hindered by t...
Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation.
BMC nephrology
Apr 22, 2025
BACKGROUND: Hospital readmission following renal transplantation significantly impacts patient outcomes and healthcare resources. While machine learning approaches offer promising solutions for risk prediction, their clinical application often lacks ...
Urinary biomarkers of kidney transplant rejection.
Current opinion in organ transplantation
Apr 2, 2025
PURPOSE OF REVIEW: Despite the introduction of many new immunosuppressive medications, allograft rejection remains a significant complication in transplantation. The use of "liquid biopsy" to evaluate allograft function and detect early rejection has...
Survival analysis using machine learning in transplantation: a practical introduction.
BMC medical informatics and decision making
Mar 21, 2025
BACKGROUND: Survival analysis is a critical tool in transplantation studies. The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making...
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