AIMC Topic: Acute Kidney Injury

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Machine learning-based prediction model for 28-day mortality in acute kidney injury patients with liver cirrhosis: A MIMIC-IV database analysis.

PloS one
BACKGROUND: Acute kidney injury (AKI) in patients with liver cirrhosis represents a significant clinical challenge with high mortality rates. This study aimed to develop and validate a machine learning-based prediction model for 28-day mortality in A...

Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation.

Annals of medicine
BACKGROUND: Acute kidney injury (AKI) is a prevalent and life-threatening condition characterized by abrupt renal function decline and subsequent inflammatory cascades. PANoptosis has emerged as a significant contributor to the pathophysiology of AKI...

Integrated single-cell and clinical transcriptomic analysis identifies blunted glycolytic activation as a hallmark of maladaptive repair in renal ischemia-reperfusion.

Renal failure
Acute kidney injury (AKI) is a common and increases risk of chronic kidney disease (CKD). While mitochondrial dysfunction drives maladaptive repair, the role of glycolysis in renal recovery remains unclear. Here, we integrated single-cell transcripto...

Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with PD-1/PD-L1: a retrospective study.

BMC medical informatics and decision making
BACKGROUND: Anti-programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunotherapy has revolutionized cancer treatment. However, it can cause immune-related adverse events, including acute kidney injury (AKI). Such adverse e...

Development and validation of risk prediction models for acute kidney disease in gout patients: a retrospective study using machine learning.

European journal of medical research
BACKGROUND: Limited research has been conducted on the prevalence of acute kidney injury (AKI) and acute kidney disease (AKD) in gout patients, as well as the impact of these renal complications on patient outcomes. This study aims to develop machine...

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...

Cellular senescence in renal ischemia-reperfusion injury.

Chinese medical journal
Acute kidney injury (AKI) affects more than 20% of hospitalized patients and is a significant contributor to morbidity and mortality, primarily due to ischemia-reperfusion injury (IRI), which is one of the leading causes of AKI. IRI not only exacerba...

GenAI exceeds clinical experts in predicting acute kidney injury following paediatric cardiopulmonary bypass.

Scientific reports
The emergence of large language models (LLMs) opens new horizons to leverage, often unused, information in clinical text. Our study aims to capitalise on this new potential. Specifically, we examine the utility of text embeddings generated by LLMs in...

Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept study.

PloS one
BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are frequent complications of hospitalization, resulting in reduced outcomes and increased cost burden. However, these conditions are only sometimes recognized and promptly treated....