Nephrology

Machine learning based hepatic safety score predicts decompensation in hepatocellular carcinoma systemic therapy.

Hepatocellular carcinoma (HCC) frequently coexists with portal hypertension, significantly increasing the risk of hepatic decompensation (HD) and variceal bleeding during systemic therapy. We developed a machine learning based hepatic safety score (MHSS) using data from 2026 patients with unresectable HCC to predict clinically significant portal hypertension (CSPH) and prognosis. A random forest m...

Integrative transcriptomic analysis identifies immune-associated candidate genes and altered immune cell infiltration in pulmonary arterial hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a progressive vascular disease characterized by immune dysregulation and pulmonary vascular remodeling. This study aimed to identify immune-associated hub genes in PAH using an integrative bioinformatics framework and to validate key candidates in an experimental model. METHODS: Three PAH lung transcriptomic datasets from the Gene Expression Omn...

Machine Learning Models for Predicting Mortality Risk and Survival Time in Lung Cancer Patients Treated with EGFR-TKIs.

This study develops machine learning models to predict patient mortality and estimate survival time using electronic health record (EHR) data from thr...

Predicting Pediatric Mortality Across Five Intensive Care Units: Toward an Early Warning Using Machine Learning.

Despite the advances in critical care and innovations of medical technology, earlier identification of children at high mortality risk remains challen...

Bridging Routine Data and Clinical Research: A Structured Approach to Data Preprocessing.

The secondary use of routine clinical data remains challenging due to heterogeneity and irregularity. We present a structured three-step preprocessing...

Human-AI Interaction in Kidney Transplant Decision Support Systems: Qualitative Study of Patient and Support Person Expectations.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in medicine, including clinical decision-making. AI-based decision support systems (D...

ATBiGRU: an interpretable temporal model for in-hospital mortality prediction in acute kidney injury.

Acute kidney injury (AKI) is associated with high mortality and healthcare burden, particularly in developing countries. Existing machine learning mod...

Integrating network pharmacology, molecular docking, machine learning, and experimental validation: puerarin improves sepsis-induced acute kidney injury via the Sirt1-Nrf2-HO-1 pathway.

Acute kidney injury (AKI) associated with sepsis has a high clinical mortality rate, and there is a lack of effective therapeutic targets; uncontrolle...

NATURal history of coronary PlaquE on cardiac computed tomography in individuals without MACE or lipid-lowering therapy: NATURE-CTstudy.

BACKGROUND: Coronary artery disease (CAD) progression has been examined mainly in cohorts enriched for major adverse cardiovascular events (MACE), a h...

Development and validation of a machine learning-based risk prediction model for hyperkalemia in patients with chronic kidney disease.

To develop an optimal predictive model for hyperkalemia in patients with chronic kidney disease (CKD). Clinical data of CKD patients were collected fr...

Life's Essential 8-Defined Cardiovascular Health and Mortality Risk in Cardiovascular-Kidney-Metabolic Health Stages 0-4: A Cohort Study.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome represents a primary contributor to global morbidity and mortality. Despite the Life's Esse...

Molecular design of electrolyte additives for aqueous zinc-ion batteries via reinforcement learning and quantum chemistry calculations.

The use of electrolyte additives is regarded as a cost-effective strategy to regulate the components of aqueous zinc-ion batteries (AZIBs) and to impr...

Optimization of artificial intelligence models for prediction of new-onset cardiovascular disease in patients with arterial hypertension.

Advanced preventive strategies are needed to decrease the burden of cardiovascular disease (CVD). We aimed to develop a predictive tool to identify in...

Eigen-guided transformer: A data-driven approach for chronic kidney disease forecasting.

Accurate prediction of Chronic Kidney Disease (CKD) development is essential for prompt therapeutic intervention; nevertheless, it is difficult due to...

Half-dose contrast media protocol using 70 kVp abdominal dynamic CT with super-resolution deep learning reconstruction: Evaluation of image quality and contrast performance.

OBJECTIVE: To evaluate contrast enhancement and image quality in 70 kVp abdominal dynamic CT using super-resolution deep learning reconstruction (SR-D...

Artificial intelligence in kidney cancer: a review of clinical applications across the disease spectrum.

PURPOSE OF REVIEW: This review examines recent advances (2024-2025) in the application of artificial intelligence (AI) to kidney cancer diagnosis, pro...

Performance of a self-attention-based model in the task of differentiating clear cell renal cell carcinoma from other renal tumors: variable Vision Transformer (vViT).

OBJECTIVES: To examine the performance of the variable Vision Transformer (vViT) in comparison with that of convolutional neural networks (CNNs) in th...

Dynamic Bayesian networks to predict loss of kidney function: a cross-institution use case in a large cohort with or at-risk of CKD.

BACKGROUND: Substantial loss of kidney function, measured as ≥40% decline in estimated glomerular filtration rate (eGFR) within a 2-year period, is as...

Relationship between the diagnostic probability of chronic kidney disease calculated using artificial intelligence-enhanced electrocardiography and the incidence of cardiovascular events.

AIMS: A low estimated glomerular filtration rate (eGFR) is the primary diagnostic criterion for chronic kidney disease (CKD), a known risk factor for ...

Xiao Zhen Fang mitigates cutaneous toxicity by inhibiting PANoptosis: Insights from untargeted metabolomics and transcriptomics.

BACKGROUND: Epidermal growth factor receptor (EGFR) inhibitors and other targeted therapies frequently cause cutaneous toxicities, impairing patient q...