Acute kidney injury (AKI) after partial nephrectomy is attributed to parenchymal reduction and ischemia, but the extent of its effect remains unclear. This study aimed to compare the incidence of postoperative AKI among surgical modalities, robot-as...
Medical error is a leading cause of patient death in the United States. Among the different types of medical errors, harm to patients caused by doctors missing early signs of deterioration is especially challenging to address due to the heterogeneity...
BACKGROUND: Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing co...
Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algorithms to augment AKI recognition using ...
Acute kidney injury (AKI) commonly occurs in hospitalized patients and can lead to serious medical complications. But it is preventable and potentially reversible with early diagnosis and management. Therefore, several machine learning based predicti...
INTRODUCTION: The purpose of this study was to analyze the epidemiological, diagnostic, therapeutic and evolutionary features of hemoglobinuria in children hospitalized in the Pediatric University Hospital Charles de Gaulle, Ouagadougou.
OBJECTIVE: Prediction of acute renal failure (ARF) and paraplegia after thoracoabdominal aortic aneurysm repair (TAAAR) is helpful for decision-making during the postoperative phase. To find a more efficient method for making a prediction, we perform...
BACKGROUND: Early diagnosis of acute kidney injury (AKI) is a major challenge in the intensive care unit (ICU). The AKIpredictor is a set of machine-learning-based prediction models for AKI using routinely collected patient information, and accessibl...