AIMC Topic: Kidney Transplantation

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Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.

Renal failure
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
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...

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
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
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
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
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
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...

Current Applications and Developments of Natural Language Processing in Kidney Transplantation: A Scoping Review.

Transplantation proceedings
BACKGROUND AND OBJECTIVE: Natural language processing (NLP) is a subfield of artificial intelligence that enables computers to process human language. As most human interactions today involve the internet and electronic devices, NLP tools quickly bec...

Multiple omics-based machine learning reveals specific macrophage sub-clusters in renal ischemia-reperfusion injury and constructs predictive models for transplant outcomes.

Computational biology and chemistry
BACKGROUND: Ischemia-reperfusion injury (IRI) is closely associated with numerous severe postoperative complications, including acute rejection, delayed graft function (DGF) and graft failure. Macrophages are central to modulating the aseptic inflamm...