AIMC Topic: Renal Dialysis

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Data-driven cluster analysis identifies three clinical phenotypes in hemodialysis patients.

Renal failure
Clinical heterogeneity among hemodialysis patients necessitates precision medicine approaches transcending conventional single-parameter management. Through machine learning analysis of 1,207 maintenance hemodialysis patients, we developed a novel tw...

Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.

Renal failure
This systematic review aimed to evaluate the performance of machine learning (ML) models and conventional statistical models (CSMs) for predicting cardiovascular events in dialysis patients. Following PRISMA guidelines, eligible studies were searched...

Plantar pressure distribution can be used to identify sarcopenia in maintenance hemodialysis patients.

Scientific reports
Patients undergoing maintenance hemodialysis (MHD) often suffer from sarcopenia, which affects their balance and significantly increases the risk of falls and death. Actively identifying sarcopenia, understanding the relationship between sarcopenia a...

A machine-learning method for predicting the 1-year risk of death in maintenance hemodialysis patients based on continuous compliance with dialysis quality indicators.

BMC nephrology
OBJECTIVE: To establish a 1-year mortality risk prediction model for maintenance hemodialysis (HD) patients using machine learning method based on the continuous assessment methods of dialysis quality indicators.

Construction and validation of a cross-sectional risk classification model for hypoproteinemia in single-center maintenance hemodialysis patient.

Scientific reports
Hypoproteinemia is a common complication across patients receiving maintenance hemodialysis (MHD). Moreover, it is associated with increased risks of cardiovascular events, infection risk, and mortality. This study aimed to construct a classification...

Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis.

BMC nephrology
BACKGROUND: Protein-energy wasting (PEW) is a common complication of patients on maintenance haemodialysis (MHD) and is strongly associated with poor clinical outcomes; early identification and timely nutritional interventions are essential. The aim ...

Construction and application of machine learning models for predicting intradialytic hypotension.

PloS one
INTRODUCTION: Intradialytic hypotension (IDH) remains a prevalent complication of hemodialysis, which is associated with adverse outcomes for patients. This study seeks to harness machine learning to construct predictive models for IDH based on multi...

Prediction Model of Intradialytic Hypertension in Hemodialysis Patients Based on Machine Learning.

Journal of medical systems
The escalating global burden of chronic kidney disease (CKD), particularly end-stage renal disease (ESRD), has intensified reliance on hemodialysis (HD), imposing substantial financial and operational burdens on healthcare systems and patients. Intra...

Pulse wave-driven machine learning for the non-invasive assessment of coronary artery calcification in patients with end-stage renal disease undergoing hemodialysis.

Biomedical engineering online
BACKGROUND: Coronary artery calcification (CAC) represents a major cardiovascular risk in patients with end-stage renal disease (ESRD) undergoing hemodialysis. Given that radial artery pulse waveforms can reflect vascular status, this study aimed to ...

Health-economic evaluation of an AI-powered decision support system for anemia management in in-center hemodialysis patients.

BMC nephrology
BACKGROUND: The Anemia Control Model (ACM) is a decision support system powered by an artificial intelligence core designed to assist nephrologists in managing anemia therapy for in-center hemodialysis (HD) patients. This study aims to evaluate the c...