Renal Dialysis

Prediction of Arteriovenous Access Dysfunction by Mel Spectrogram-based Deep Learning Model.

International journal of medical sciences 39310268

The early detection of arteriovenous (AV) access dysfunction is crucial for maintaining the patency of vascular access. This study aimed to use deep learning to predict AV access malfunction necessitating further vascular management. This prospecti...

Aged Arteriovenous Shunt, Surgical Deep Learning Female Humans Male Middle Aged Neural Networks, Computer Prospective Studies Renal Dialysis ROC Curve Sound Spectrography

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The application of blood flow sound contrastive learning to predict arteriovenous graft stenosis of patients with hemodialysis.

PloS one 39150934

End-stage kidney disease (ESKD) presents a significant public health challenge, with hemodialysis (HD) remaining one of the most prevalent kidney replacement therapies. Ensuring the longevity and functionality of arteriovenous accesses is challenging...

Aged Arteriovenous Shunt, Surgical Constriction, Pathologic Female Graft Occlusion, Vascular Humans Kidney Failure, Chronic Machine Learning Male Middle Aged Neural Networks, Computer Renal Dialysis Sound

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Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals.

Sensors (Basel, Switzerland) 39338665

This research examines the application of non-invasive acoustic analysis for detecting obstructions in vascular access (fistulas) used by kidney dialysis patients. Obstructions in these fistulas can interrupt essential dialysis treatment. In this stu...

Constriction, Pathologic Deep Learning Female Humans Male Middle Aged Neural Networks, Computer Renal Dialysis Signal Processing, Computer-Assisted Sound Vascular Access Devices

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Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.

Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy 39327762

INTRODUCTION: The elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accurate predictive tools. This study developed two models for predicting all-cause mortality and time to ...

Aged Cause of Death Cohort Studies Female Humans Kidney Failure, Chronic Logistic Models Machine Learning Male Middle Aged Renal Dialysis Retrospective Studies Support Vector Machine Time Factors

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Using machine learning models for predicting monthly iPTH levels in hemodialysis patients.

Computer methods and programs in biomedicine 39637702

BACKGROUND AND OBJECTIVE: Intact parathyroid hormone (iPTH), also known as active parathyroid hormone, is an important indicator commonly for monitoring secondary hyperparathyroidism (SHPT) in patients undergoing hemodialysis. The aim of this study w...

Female Humans Hyperparathyroidism, Secondary Machine Learning Male Parathyroid Hormone Predictive Value of Tests Registries Renal Dialysis Retrospective Studies Risk Assessment Taiwan Time Factors

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Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.

Scientific reports 39609495

Early mortality after hemodialysis (HD) initiation significantly impacts the longevity of HD patients. This study aimed to quantify the effect sizes of risk factors on mortality using various machine learning approaches. A cohort of 3284 HD patients ...

Adult Aged Deep Learning Female Humans Kidney Failure, Chronic Male Middle Aged Prospective Studies Renal Dialysis Republic of Korea Risk Factors Survival Analysis

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Prediction of dialysis adequacy using data-driven machine learning algorithms.

Renal failure 39526333

BACKGROUND: Adequate delivery of hemodialysis (HD), measured by the spKt/V derived from urea reduction, is an important determinant of clinical outcomes in chronic hemodialysis patients. However, the need for pre- and postdialysis blood samples preve...

Adult Aged Algorithms Female Humans Kidney Failure, Chronic Machine Learning Male Middle Aged Renal Dialysis Retrospective Studies Urea

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Optimizing anemia management using artificial intelligence for patients undergoing hemodialysis.

Scientific reports 39500941

Patients with end-stage kidney disease (ESKD) frequently experience anemia, and maintaining hemoglobin (Hb) levels within a targeted range using erythropoiesis-stimulating agents (ESAs) is challenging. This study introduces a gated recurrent unit-att...

Aged Anemia Artificial Intelligence Erythrocyte Transfusion Female Hematinics Hemoglobins Humans Kidney Failure, Chronic Machine Learning Male Middle Aged Renal Dialysis Retrospective Studies

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Predicting high-flow arteriovenous fistulas and cardiac outcomes in hemodialysis patients.

Journal of vascular surgery 39631475

BACKGROUND: Heart failure is common in patients receiving hemodialysis. A high-flow arteriovenous fistula (AVF) may represent a modifiable risk factor for heart failure and death. Currently, no tools exist to assess the risk of developing a high-flow...

Aged Arteriovenous Shunt, Surgical Australia Blood Flow Velocity Decision Support Techniques Female Heart Failure Humans Machine Learning Male Middle Aged Predictive Value of Tests Regional Blood Flow Renal Dialysis Retrospective Studies Risk Assessment Risk Factors Time Factors Treatment Outcome

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Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

Journal of the American Medical Informatics Association : JAMIA 39545362

OBJECTIVES: This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretabi...

Adult Attitude of Health Personnel Caregivers Clinical Decision-Making Decision Making, Shared Female Focus Groups Humans Machine Learning Male Middle Aged Renal Dialysis Trust

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Explore the latest research on artificial intelligence and machine learning in medicine.

Prediction of Arteriovenous Access Dysfunction by Mel Spectrogram-based Deep Learning Model.

The application of blood flow sound contrastive learning to predict arteriovenous graft stenosis of patients with hemodialysis.

Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals.

Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.

Using machine learning models for predicting monthly iPTH levels in hemodialysis patients.

Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.

Prediction of dialysis adequacy using data-driven machine learning algorithms.

Optimizing anemia management using artificial intelligence for patients undergoing hemodialysis.

Predicting high-flow arteriovenous fistulas and cardiac outcomes in hemodialysis patients.

Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

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