Nephrology

Machine learning selection of basement membrane-associated genes and development of a predictive model for kidney fibrosis.

This study investigates the role of basement membrane-related genes in kidney fibrosis, a significant factor in the progression of chronic kidney disease that can lead to end-stage renal failure. The authors aim to develop a predictive model using machine learning techniques due to the limitations of existing diagnostic methods, which often lack sensitivity and specificity. Utilizing gene expressi...

Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.

BACKGROUND: The principles of urgency, utility, and benefit are fundamental concepts guiding the ethical and practical decision-making process for organ allocation; however, LT allocation still follows an urgency model.

Development of a Machine Learning-Powered Optimized Lung Allocation System for Maximum Benefits in Lung Transplantation: A Korean National Data.

BACKGROUND: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. Thi...

Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model.

Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been strongly associated with chronic kidney disease (...

Measuring kidney stone volume - practical considerations and current evidence from the EAU endourology section.

PURPOSE OF REVIEW: This narrative review provides an overview of the use, differences, and clinical impact of current methods for kidney stone volume ...

Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis - based on machine learning algorithms.

This study aims to investigate the association between visit-to-visit blood pressure variability (VVV) in early stage of continuous ambulatory periton...

Navigating advanced renal cell carcinoma in the era of artificial intelligence.

BACKGROUND: Research has helped to better understand renal cell carcinoma and enhance management of patients with locally advanced and metastatic dise...

A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.

Traditional methods for diagnosing chronic kidney disease (CKD) via laboratory data may not be capable of identifying early kidney disease. Kidney bio...

Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.

OBJECTIVE: Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN), are common c...

Key RNA-binding proteins in renal fibrosis: a comprehensive bioinformatics and machine learning framework for diagnostic and therapeutic insights.

BACKGROUND: Renal fibrosis is a critical factor in chronic kidney disease progression, with limited diagnostic and therapeutic options. Emerging evide...

Advanced prognostic modeling with deep learning: assessing long-term outcomes in liver transplant recipients from deceased and living donors.

BACKGROUND: Predicting long-term outcomes in liver transplantation remain a challenging endeavor. This research aims to harness the power of deep lear...

Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists.

PURPOSE: Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory...

Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.

BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs),...

Perioperative goal-directed therapy with artificial intelligence to reduce the incidence of intraoperative hypotension and renal failure in patients undergoing lung surgery: A pilot study.

STUDY OBJECTIVE: The aim of this study was to investigate whether goal-directed treatment using artificial intelligence, compared to standard care, ca...

Supercapacitor Materials Database Generated using Web Scrapping and Natural Language Processing.

Electrochemical energy storage plays a vital role in achieving environmental sustainability. Supercapacitors emerge as promising alternatives to batte...

Liver fibrosis progression analyzed with AI predicts renal decline.

BACKGROUND & AIMS: The relationship between biopsy-proven liver fibrosis progression and renal function decline in patients with metabolic dysfunction...

Molecular structure of NRG-1 protein and its impact on adult hypertension and heart failure: A new clinical Indicator diagnosis based on advanced machine learning.

The purpose of this study was to investigate the molecular structure of NRG-1 protein and its mechanism of action in adult hypertensive heart failure....

Machine learning prediction of glaucoma by heavy metal exposure: results from the National Health and Nutrition Examination Survey 2005 to 2008.

Using follow-up data from the National Health and Nutrition Examination Survey (NHANES) database, we have collected information on 2572 subjects and u...

Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients.

Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with...

Discovering Vitamin-D-Deficiency-Associated Factors in Korean Adults Using KNHANES Data Based on an Integrated Analysis of Machine Learning and Statistical Techniques.

: Vitamin D deficiency (VDD) is a global health concern associated with metabolic disease and immune dysfunction. Despite known risk factors like limi...