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Renal Insufficiency, Chronic

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A multi-modal fusion model with enhanced feature representation for chronic kidney disease progression prediction.

Briefings in bioinformatics
Artificial intelligence (AI)-based multi-modal fusion algorithms are pivotal in emulating clinical practice by integrating data from diverse sources. However, most of the existing multi-modal models focus on designing new modal fusion methods, ignori...

TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision mana...

Application of Artificial Intelligence in Clinical Practice - Perception of a Multinational Group of Nephrologists.

Studies in health technology and informatics
This study investigates the perception of a multinational group of nephrologists on artificial intelligence (AI) application in clinical practice. A validated on-line survey was performed in March 2024, in 4 continents. The results revealed a prevale...

Learning Enabled Control for Optimal EPO Dosage in Virtual CKD Patients: Case of Bleeding and Missing Dosage.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Traditional model-based control methods require predictive models to design control policies. These models often suffer limitations on dimensionality, uncertainty, and unmodeled dynamics. This affects the performance of control policy, especially, pe...

Ensemble Learning Approaches for Automatic Detection of Chronic Kidney Disease Stages during Sleep.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study investigates the use of ensemble learning methods for the automatic detection of chronic kidney disease (CKD) stages during sleep. We applied and evaluated four ensemble learning approaches-CatBoost, random forest, XGBoost, and LightGBM-to...

Towards early detection of chronic kidney disease based on gait patterns: IMU-based approach using neural networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The aging population has led to an increased prevalence of chronic kidney disease (CKD), associated with a higher incidence of gait disturbances and rise in fall rates. It is important that early detection and continuous monitoring of CKD to improve ...

Deep Learning-Based Assessment of Built Environment From Satellite Images and Cardiometabolic Disease Prevalence.

JAMA cardiology
IMPORTANCE: Built environment plays an important role in development of cardiovascular disease. Large scale, pragmatic evaluation of built environment has been limited owing to scarce data and inconsistent data quality.

Application of improved glomerular filtration rate estimation by a neural network model in patients with neurogenic lower urinary tract dysfunction.

Clinical nephrology
BACKGROUND: Previous studies have indicated that creatinine (Cr)-based glomerular filtration rate (GFR) estimating equations - including the new Chronic Kidney Disease Epidemiology creatinine (CKD-EPI) equation without race and the estimated glomerul...

Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis.

Bioinformatics (Oxford, England)
MOTIVATION: Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diag...

Can Simplified PADUA Renal (SPARE) Nephrometry scoring system help predict renal function outcomes after robot-assisted partial nephrectomy? (UroCCR study 93).

Minerva urology and nephrology
BACKGROUND: The SPARE Nephrometry Score (NS) is described as easier to implement than the RENAL and PADUA NSs, currently more widely used. Our objective was to compare the accuracy of SPARE NS in predicting renal function outcomes following RAPN.