AIMC Topic: Renal Insufficiency, Chronic

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Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis.

Renal failure
INTRODUCTION: Natural language processing (NLP) has been used to analyze unstructured imaging report data, yet its application in identifying chronic kidney disease (CKD) features from kidney ultrasound reports remains unexplored.

Clinical characteristics and CKD care delivery in African American and American Indian or Alaska Native patients: A real-world cohort study.

BMC nephrology
BACKGROUND: Racially minoritized populations in the United States (US), notably African American (AA) and American Indian/Alaska Native (AI/AN), experience disproportionately higher rates of chronic kidney disease (CKD), diabetes, and hypertension co...

Driven early detection of chronic kidney cancer disease based on machine learning technique.

PloS one
In recent times, chronic kidney cancer has been considered a significant cause of cancer, and Renal Cell Carcinoma (RCC) has become a significant prevalent among various kidney cancer conditions. The analysis of kidney cancer, an important and often ...

A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology.

Renal failure
Artificial intelligence (AI) and machine learning (ML) are transforming nephrology by enhancing diagnosis, risk prediction, and treatment optimization for conditions such as acute kidney injury (AKI) and chronic kidney disease (CKD). AI-driven models...

Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015-2018.

BMC public health
Cardiometabolic Multimorbidity (CMM) has emerged as one of the primary threats to human health globally due to its high incidence, disability, and mortality rates. Accurate identification of CMM patterns is crucial for CMM classification and health m...

Exploring the association between volatile organic compound exposure and chronic kidney disease: evidence from explainable machine learning methods.

Renal failure
BACKGROUND: Chronic Kidney Disease (CKD) affects approximately 697.5 million people worldwide. Volatile organic compounds (VOCs) are emerging as potential risk factors, but their complex relationships with CKD may be underestimated by traditional lin...

Leveraging pulse wave signal properties for coronary artery calcification screening in CKD patients.

Computers in biology and medicine
BACKGROUND AND AIMS: Chronic kidney disease (CKD) patients are particularly susceptible to coronary atherosclerosis, which can be assessed using computed tomography (CT)-based coronary artery calcium (CAC) score. However, such a costly examination mi...

Machine learning-based prediction model for cognitive impairment risk in patients with chronic kidney disease.

PloS one
BACKGROUND: The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.

Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.

Journal of health, population, and nutrition
BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiovascular system, chronic kidney disease, and metabolic risk factors. In China, the prevalence of CKM i...

Development Of the VAMPCT Score for Predicting Mortality in CKD Patients with COVID-19.

International journal of medical sciences
Chronic kidney disease (CKD) patients with coronavirus disease 2019 (COVID-19) are at significant risk of death. However, clinical identification of high-risk individuals remains suboptimal despite the recognition of many pathophysiological and como...