AIMC Topic: Kidney Failure, Chronic

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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...

Artificial intelligence-driven kidney organ allocation: systematic review of clinical outcome prediction, ethical frameworks, and decision-making algorithms.

BMC nephrology
Kidney transplantation remains the optimal treatment for end-stage renal disease, yet persistent organ shortages and inequitable allocation necessitate innovative solutions. Artificial intelligence (AI) and machine learning (ML) have emerged as promi...

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.

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 ...

Non-invasive assessment techniques for renal fibrosis: advances and perspectives.

Renal failure
Renal fibrosis is a critical pathological process driving chronic kidney disease (CKD) and end-stage renal disease (ESRD). Early diagnosis is essential for timely intervention, yet traditional methods like renal biopsy are invasive and present signif...

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 ...

An improved domain-adversarial network for predicting hemodialysis adequacy.

Biomedical physics & engineering express
Hemodialysis (HD) is the primary life-sustaining treatment for patients with end-stage renal disease (ESRD). However, current real-time monitoring methods during dialysis are costly, complex, and not widely adopted. Therefore, this study aims to prop...

An artificial intelligence model to predict mortality among hemodialysis patients: A retrospective validated cohort study.

Scientific reports
Hemodialysis stands as the most prevalent renal replacement therapy globally. Accurately identifying mortality among hemodialysis patients is paramount importance, as it enables the formulation of tailored interventions and facilitates timely managem...