Nephrology

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

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RNAseq-Based Machine Learning Models for Prognostication of Multiple Myeloma

Multiple myeloma (MM) is characterized by abnormal plasma cell proliferation in the bone marrow, lea...

InfEHR: Resolving Clinical Uncertainty through Deep Geometric Learning on Electronic Health Records

Electronic health records (EHRs) contain multimodal data that can inform diagnostic and prognostic c...

Deep generative models for vessel segmentation in CT angiography of the brain

Automated vessel segmentation in brain CT angiography (CTA) remains challenging despite the potentia...

MRI-Derived Variables Combined with Machine Learning for Pulmonary Hypertension Risk Prediction: A Retrospective Analysis

Pulmonary hypertension (PH) is a severe and progressive vascular disease for which early diagnosis a...

Impact of Iron Deficiency on Clinical Outcomes in Congestive Heart Failure: A Retrospective Analysis of Risk Stratification and Mortality

Iron deficiency frequently coexists with congestive heart failure, thereby increasing morbidity and ...

Transformer-based multiclass segmentation pipeline for basic kidney histology

Multiclass segmentation of microanatomy in kidney biopsies is an important and non-trivial task in c...

Spine age estimation using deep learning in lateral spine radiographs and DXA VFA to predict incident fracture and mortality

Spine age estimated from lateral spine radiographs and DXA vertebral fracture assessments (VFAs) cou...

Utilizing Machine Learning Models to Predict Acute Kidney Injury in Septic Patients from MIMIC-III Database

Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This react...

Artificial Intelligence Prediction of Age from Echocardiography as a Marker for Cardiovascular Disease

Accurate understanding of biological aging and the impact of environmental stressors is crucial for ...

Artificial Intelligence for Chronic Kidney Disease Early Detection and Prognosis

The integration of Artificial Intelligence (AI) in the early detection and prognosis of Chronic Kidn...

Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines

The analysis of complex biomedical datasets is becoming central to understanding disease mechanisms,...

Predicting 28-Day Mortality in First-Time ICU Patients with Heart Failure and Hypertension Using LightGBM: A MIMIC-IV Study

Heart Failure (HF) and Hypertension (HTN) are common yet severe cardiovascular conditions, both of w...

Computational characterization of lymphocyte topology on whole slide images of glomerular diseases

The complexity of distribution of inflammatory cells in the kidney is not well captured by conventio...

Prompts to Table: Specification and Iterative Refinement for Clinical Information Extraction with Large Language Models

Extracting structured data from free-text medical records at scale is laborious, and traditional app...

In silico perturbations provide multivariate interpretability in predicting post-lung transplant outcomes

Lung transplantation is a life-saving therapy for end-stage lung disease but has the poorest surviva...

Human-level information extraction from clinical reports with fine-tuned language models

Extracting structured data from clinical notes remains a key bottleneck in clinical research. We hyp...

Clinical phenotypes in hypertension: a data-driven approach to risk stratification and outcome prediction

Hypertension (HTN) is a major contributor to cardiovascular (CV) morbidity and mortality. Its hetero...

Plasma Cell-Free RNA Captures Immune Dynamics and Predicts GVHD after Hematopoietic Stem Cell Transplantation

Despite long-standing success of hematopoietic stem cell transplantation (HSCT) in the treatment of ...

Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome: a prospective cohort study

Sepsis therapy is still limited to treatment of the underlying infection and supportive measures. To...

TrialGenie: Empowering Clinical Trial Design with Agentic Intelligence and Real World Data

Clinical trial design (CTD) is a time-consuming process that requires substantial domain expertise. ...

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