Nephrology

Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension.

INTRODUCTION: There are limited data available regarding the connection between heavy metal exposure...

Development and experimental validation of hypoxia-related gene signatures for osteosarcoma diagnosis and prognosis based on WGCNA and machine learning.

Osteosarcoma (OS) is the most common primary malignant tumour of the bone with high mortality. Here,...

Coagulation Risk Predicting in Anticoagulant-Free Continuous Renal Replacement Therapy.

INTRODUCTION: Continuous renal replacement therapy (CRRT) is a prolonged continuous extracorporeal b...

Hierarchical Hybrid Networks for Automatic Pulmonary Blood Vessel Segmentation in Computed Tomography Images.

Pulmonary arterial hypertension (PAH) is considered the third most common cardiovascular disease aft...

Ensemble of machine learning techniques to predict survival in kidney transplant recipients.

Kidney transplant recipients face a high cardiovascular risk, which is a leading cause of death in t...

Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

PRCIS: We developed unsupervised machine learning models to identify different subtypes of patients ...

Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images.

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal c...

APOD: A biomarker associated with oxidative stress in acute rejection of kidney transplants based on multiple machine learning algorithms and animal experimental validation.

BACKGROUND: Oxidative stress is an unavoidable process in kidney transplantation and is closely rela...

Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.

BACKGROUND: Understanding the complex interactions between genes and their causal effects on disease...

Insights into ChatGPT application in renal transplantation.

Effects of environmental phenols on eGFR: machine learning modeling methods applied to cross-sectional studies.

PURPOSE: Limited investigation is available on the correlation between environmental phenols' exposu...

Construction of Risk-Prediction Models for Autogenous Arteriovenous Fistula Thrombosis in Patients on Maintenance Hemodialysis.

INTRODUCTION: Autogenous arteriovenous fistula (AVF) is the preferred vascular access in patients un...

Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.

BACKGROUND: With the development of artificial intelligence, the application of machine learning to ...

Protocol for machine-learning-based 3D image analysis of nuclear envelope tubules in cultured cells.

The nuclear envelope can form complex structures in physiological and pathological contexts. Current...

Predictive modeling of mortality in carbapenem-resistant bloodstream infections using machine learning.

, a notable drug-resistant bacterium, often induces severe infections in healthcare settings, prompt...

Ppb-Level Ammonia Sensor for Exhaled Breath Diagnosis Based on UV-DOAS Combined with Spectral Reconstruction Fitting Neural Network.

Ammonia (NH) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measureme...

Using machine learning to predict the risk of short-term and long-term death in acute kidney injury patients after commencing CRRT.

BACKGROUND: The mortality rate and prognosis of short-term and long-term acute kidney injury (AKI) p...

Quality of Information About Kidney Stones from Artificial Intelligence Chatbots.

Kidney stones are common and morbid conditions in the general population with a rising incidence gl...

Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors.

Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural...

Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models.

The decision to accept a deceased donor organ offer for transplant, or wait for something potentiall...

Prediction of post-donation renal function using machine learning techniques and conventional regression models in living kidney donors.

BACKGROUND: Accurate prediction of renal function following kidney donation and careful selection of...