Sepsis-associated acute kidney injury (SA-AKI) patients in the ICU often suffer from sepsis-associated delirium (SAD), which is linked to unfavorable outcomes. This research aimed to develop a machine learning-based model for early SAD prediction in ...
BACKGROUND: Cardiac valve calcification (CVC) is common in dialysis patients and associated with increased cardiovascular risk. However, early screening has been limited by cost concerns. This study aimed to develop and validate a machine learning mo...
BACKGROUND: Reducing postoperative cardiovascular and neurological complications (PCNC) during thoracic surgery is the key to improving postoperative survival.
BACKGROUND: Chronic hepatitis B (CHB) is a common cause of liver cirrhosis (LC), a condition associated with an unfavourable prognosis. Therefore, timely diagnosis of LC in CHB patients is crucial.
BACKGROUND: Adequate bowel preparation is crucial for effective colonoscopy, especially in elderly patients who face a high risk of inadequate preparation. This study develops and validates a machine learning model to predict bowel preparation adequa...
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 ...
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...
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 ...
STUDY OBJECTIVE: Sensitivity for stroke detection in emergency medical communication centers (EMCCs) varies widely. Few studies offer detailed insights into the out-of-hospital pathways of patients with stroke. This study explored the ability of EMCC...
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.
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