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Kidney Transplantation

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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...

Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish.

PloS one
INTRODUCTION: Accurate identification of graft loss in Electronic Medical Records of kidney transplant recipients is essential but challenging due to inconsistent and not mandatory International Classification of Diseases (ICD) codes. We developed an...

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...

Applying Artificial Intelligence to Quantify Body Composition on Abdominal CTs and Better Predict Kidney Transplantation Wait-List Mortality.

Journal of the American College of Radiology : JACR
BACKGROUND: Prekidney transplant evaluation routinely includes abdominal CT for presurgical vascular assessment. A wealth of body composition data are available from these CT examinations, but they remain an underused source of data, often missing fr...

Identifying Risk Factors for Graft Failure due to Chronic Rejection < 15 Years Post-Transplant in Pediatric Kidney Transplants Using Random Forest Machine-Learning Techniques.

Pediatric transplantation
BACKGROUND: Chronic rejection forms the leading cause of late graft loss in pediatric kidney transplant recipients. Despite improvement in short-term graft outcomes, chronic rejection impedes comparable progress in long-term graft outcomes.

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 ...

Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection.

Current opinion in organ transplantation
PURPOSE OF REVIEW: The objective of this review is to provide an update on the application of artificial intelligence (AI) for the histological interpretation of kidney transplant biopsies.