BACKGROUND: Machine learning (ML) has the potential to enhance performance by capturing nonlinear interactions. However, ML-based models have some limitations in terms of interpretability.
This study aims to develop optimal predictive models for perioperative neurocognitive disorders (PND) in hip arthroplasty patients, thereby advancing clinical practice. Data from all hip arthroplasty patients in the MIMIC-IV database were utilized to...
BACKGROUND: Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients' prognosis.
PURPOSE OF REVIEW: Perioperative risk scores aim to risk-stratify patients to guide their evaluation and management. Several scores are established in clinical practice, but often do not generalize well to new data and require ongoing updates to impr...
BACKGROUND: Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalations (UCE) following post-anesthesia care unit (PACU) discharge. Studies indicate patient risk factors ...
BACKGROUND: Machine learning (ML) is a type of artificial intelligence (AI) and has been utilized in clinical research and practice to construct high-performing prediction models. Hidden blood loss (HBL) is prevalent during the perioperative period o...
BACKGROUND: Kidney autotransplantation (KAT) is the ultimate way to salvage kidneys with complex renovascular, ureteral, or malignant pathologies that are not amenable to in situ reconstruction. A minimally invasive approach could broaden its adoptio...
INTRODUCTION: A prospective study was designed to elucidate incidence and predictors of deep venous thrombosis (DVT) in patients undergoing craniotomies.
BACKGROUND: Simultaneous division of the splenic artery, splenic vein and pancreatic parenchyma during laparoscopic distal pancreatosplenectomy (LDPS) is known as the lasso technique, which is considered to be simple to perform. However, the original...
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