Nephrology

Latest AI and machine learning research in nephrology for healthcare professionals.

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Artificial Intelligence to Improve Blood Pressure Control: A State-of-the-Art Review.

Hypertension remains a major global health challenge, contributing to significant morbidity and mort...

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for ...

Novel composite health assessment risk model for older allogeneic transplant recipients: BMT-CTN 1704.

Allogeneic hematopoietic cell transplantation (allo-HCT) is potentially curative for older adults wi...

Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.

INTRODUCTION: Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplan...

Molecule-Responsive SERS Sensors for Urine Diagnosis of Kidney Diseases Enhanced by Neural Networks.

Early diagnosis of kidney disease is crucial for treatment and prognosis. Compared with kidney biops...

Liver Fat Fraction and Machine Learning Improve Steatohepatitis Diagnosis in Liver Transplant Patients.

Machine learning identifies liver fat fraction (FF) measured by H MR spectroscopy, insulinemia, and ...

Deep learning-based histopathologic segmentation of peritubular capillaries in kidney transplant biopsies.

BACKGROUND: Assessing the extent of inflammation in peritubular capillaries (PTCs) is important for ...

Development and validation of an interpretable machine learning model for predicting hyperuricemia risk: Based on environmental chemical exposure.

Hyperuricemia is a global health concern, with environmental chemicals as risk factors. This study u...

An informed machine learning based environmental risk score for hypertension in European adults.

BACKGROUND: The exposome framework seeks to unravel the cumulated effects of environmental exposures...

Quantitative CT biomarkers for renal cell carcinoma subtype differentiation: a comparison of DECT, PCT, and CT texture analysis.

OBJECTIVE: To evaluate and compare the diagnostic performance of CT texture analysis (CTTA), perfusi...

Discover important donor-recipient risk factors and interactions in heart transplant primary graft dysfunction with machine learning.

OBJECTIVES: Primary graft dysfunction (PGD) is an essential outcome after the heart transplant, whic...

MACHINE LEARNING AND BIOINFORMATICS TO IDENTIFY COAGULATION BIOMARKERS IN SEPSIS-RELATED KIDNEY INJURY.

Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with m...

MACHINE LEARNING AND SHOCK INDICES-DERIVED SCORE FOR PREDICTING CONTRAST-INDUCED NEPHROPATHY IN ACUTE CORONARY SYNDROME PATIENTS.

Background: Contrast-induced nephropathy (CIN) is a serious complication following acute coronary sy...

Use of machine learning techniques to predict poor survival after hematopoietic cell transplantation for myelofibrosis.

With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes...

Label-free urinary protein detection through machine learning analysis of single droplet evaporation patterns.

BACKGROUND: Chronic kidney disease (CKD) is a major global public health issue, with a steadily incr...

A Spatial Metabolomics Annotation Workflow Leveraging Cyclic Ion Mobility and Machine Learning-Predicted Collision Cross Sections.

In nontargeted spatial metabolomics, accurate annotation is crucial for understanding metabolites' b...

From planning to prognosis: predicting renal function after minimally-invasive partial nephrectomy with artificial intelligence.

This study presents a machine learning model to predict renal function decline following minimally-i...

Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction.

Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate....

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