PURPOSE: This study aims to develop and evaluate machine learning (ML) models to predict the likelihood of hospital readmission within 30 days after discharge for patients with heart failure (HF). The goal is to compare the predictive accuracy of ML ...
BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a leading cause of maternal and fetal mortality worldwide. Early detection and risk stratification are critical for timely intervention to prevent severe complications such as eclampsia, strok...
Endoscopic retrograde cholangiopancreatography (ERCP) is a widely used diagnostic and therapeutic procedure for pancreaticobiliary diseases. However, its relatively invasive nature necessitates a thorough understanding of potential adverse events and...
Using the 2011 baseline data of the China health and retirement longitudinal study, we examined the associations between serum lipids and other risk factors and incident stroke, and developed and compared multiple machine learning models for stroke-r...
This study aimed to evaluate the effectiveness of navigation nurse management (NNM) in orthopedic perioperative care and develop machine learning (ML) models to predict postoperative recovery quality. We sought to identify key factors influencing rec...
Ureteroscopic lithotripsy has emerged as the cornerstone treatment modality for ureteral stones due to its exceptional success rates and minimal complication profiles. Nevertheless, postoperative urinary tract infection (UTI) remains a prevalent and ...
BACKGROUND AND OBJECTIVES: Aneurysm risk prediction remains an imprecise science that places patients at risk for either over or undertreatment. Machine learning (ML) models may improve clinical practice by adding precision to risk assessment. This s...
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Nov 1, 2025
OBJECTIVE: To develop and validate advanced machine learning (ML) models for predicting unplanned intrapartum cesarean deliveries in women with no previous cesarean delivery, using both static and dynamic clinical data.
BACKGROUND: The Outpatient Arthroplasty Risk Assessment (OARA) Score was developed to risk-stratify patients for safe same-day discharge outpatient total joint arthroplasty (TJA). It has demonstrated predictive ability for length of stay in primary T...
BACKGROUND: Recent evidence has shown that machine learning (ML) techniques can accurately forecast adverse cardiovascular and limb events in patients with intermittent claudication. This is the first study to compare the predictive performance of ML...
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