Nephrology

MedSR-Vision: Deep Learning Framework for Multi-Domain Medical Image Super-Resolution

Medical image super-resolution (MedSR) is essential for improving diagnostic precision across diverse imaging modalities such as MRI, CT, X-ray, Ultrasound, and Fundus imaging. Despite rapid advances in deep learning, challenges remain in preserving anatomical accuracy, maintaining perceptual quality, and generalizing across medical domains. This paper presents MedSR-Vision, a novel unified deep l...

Development and Validation of Machine Learning Models for Predicting Mortality in Hospitalised Systemic Lupus Erythematosus Patients in Dr. Sardjito Hospital, Indonesia Machine Learning Prediction of In-Hospital Mortality in SLE

Objectives This study aimed to develop and validate machine learning models to predict in-hospital mortality among systemic lupus erythematosus (SLE) patients using administrative claims data in a tertiary referral center in Indonesia. Methods We conducted a retrospective cohort study of 327 SLE hospital admissions between January 2019 and June 2025. Predictor variables included demographics, hosp...

Differentiable latent structure discovery for interpretable forecasting in clinical time series

Background: Timely, uncertainty-aware forecasting from irregular electronic health records (EHR) can support critical-care decisions, yet most approac...

Echo-α: Large Agentic Multimodal Reasoning Model for Ultrasound Interpretation

Ultrasound interpretation requires both precise lesion localization and holistic clinical reasoning, yet existing methods typically excel at only one ...

SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasound

Robotic ultrasound has advanced local image-driven control, contact regulation, and view optimization, yet current systems lack the anatomical underst...

Exposome-Based Clustering of Urinary VOC and PAH Biomarkers Reveals Racially Patterned Cardiovascular Risk in a Nationally Representative US Cohort: A Machine Learning Analysis of NHANES 2017-2018

Background Polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs) are combustion-derived pollutants linked to cardiovascular di...

CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification

Motivation: Multi-omics integration can improve cancer subtyping, but modality informativeness and noise vary across cancer types and patients. Existi...

Dialysis Risk Prediction and Treatment Effect Estimation for AKI patients using Longitudinal Electronic Health Records

Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures in...

Housekeeping Gene Expression Normalization in Transcriptomics Mitigates Data Leakage in Machine Learning Models

Background: Inappropriate normalization can lead to data leakage and overfitting in machine learning models. Accurately identifying housekeeping genes...

Patient perspectives on living with hypertension: Social media listening analysis across predominantly high-income countries

Background: Chronic conditions such as hypertension can significantly disrupt daily life and emotional wellbeing. The interaction between patients' pe...

Accessible and Reproducible Renal Cell Carcinoma Research Through Open-Sourcing Data and Annotations

Background: Medical imaging, especially computed tomography and magnetic resonance imaging, is essential in clinical care of patients with renal cell ...

Glio-SERS: Label-Free Molecular Profiling of Plasma Extracellular Vesicles in Brain Tumors Using SERS and Artificial Intelligence

Extracellular vesicles are increasingly recognized as important carriers of disease-associated molecular information, yet robust methods for their iso...

Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury

Accurate early prediction of Acute Kidney Injury (AKI) is critical for timely clinical intervention. However, existing deep learning models struggle w...

Multi-Objective Reinforcement Learning for Generating Covalent Inhibitor Candidates

Rational design of covalent inhibitors requires simultaneously optimizing multiple properties, such as binding affinity, target selectivity, or electr...

Vector2Variant: Discovery of Genetic Associations from ML Derived Representations without Phenotype Engineering

Genome-wide association studies (GWAS) have transformed our understanding of human biology, but are constrained by the need for predefined phenotypes....

Performance of open-source large language models on nephrology self-assessment program

Background: Large Language Models (LLMs) have demonstrated strong performance in medical question-answering tasks, highlighting their potential for cl...

Testing and Estimating Causal Treatment Effect Heterogeneity in Observational Studies via Revised Deep Semiparametric Regression: A Lung Transplant Case Study

Lung transplantation programs must decide when bilateral lung transplantation (BLT) offers meaningful functional benefit over single lung transplantat...

Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease

Hybrid Quantum Neural Networks (HQNNs) have recently emerged as a promising paradigm for near-term quantum machine learning. However, their practical ...

Integrated kidney and urine proteomics define encrypted antimicrobial peptides as effectors of host defence in human pyelonephritis

Antimicrobial peptides (AMPs) are key effectors of host defence, however, their functional deployment across renal tissue and urine in pyelonephritis ...

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI

Adaptive medical AI models often face performance drops in dynamic clinical environments due to data drift. We propose an autonomous continuous monito...