BACKGROUND: With the development of artificial intelligence, the application of machine learning to develop predictive models for sepsis-associated acute kidney injury has made potential breakthroughs in early identification, grading, diagnosis, and ...
BACKGROUND: The mortality rate and prognosis of short-term and long-term acute kidney injury (AKI) patients who undergo continuous renal replacement therapy (CRRT) are different. Setting up risk stratification tools for both short-term and long-term ...
BMC medical informatics and decision making
39054463
INTRODUCTION: Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-based clinical model to predict 1-year mortality in patients with SA-AKI.
Orthopaedics & traumatology, surgery & research : OTSR
39047862
INTRODUCTION: Total knee arthroplasty (TKA) is a procedure associated with risks of electrolyte and kidney function disorders, which are rare but can lead to serious complications if not correctly identified. A routine check-up is very often carried ...
PURPOSE OF REVIEW: This review explores the transformative advancement, potential application, and impact of artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), on critical care nephrology.
Studies in health technology and informatics
39176914
Causal Deep/Machine Learning (CDL/CML) is an emerging Artificial Intelligence (AI) paradigm. The combination of causal inference and AI could mine explainable causal relationships between data features, providing useful insights for various applicati...
Studies in health technology and informatics
39176729
Early identification of patients at high risk of cardiac surgery-associated acute kidney injury (CSA-AKI) is crucial for its prevention. We aimed to leverage perioperative clinical and intraoperative biosignal data to develop machine learning models ...
BACKGROUND: To construct and evaluate a predictive model for in-hospital mortality among critically ill patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT), based on nine machine learning (ML) algorithm.
BACKGROUND: Anaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment.