Background: The utilization of artificial intelligence and machine learning as diagnostic and
predictive tools in perioperative medicine holds great promise. Indeed, many studies have …

This narrative review explores the utilization of machine learning (ML) and artificial
intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer …

Postoperative delirium (POD) is a common and severe complication in elderly patients with
hip fractures. Identifying high-risk patients with POD can help improve the outcome of …

Study objective Postoperative delirium is associated with morbidity and mortality, and its
incidence varies widely. Using known predisposing and precipitating factors, we sought to …

Objectives Pediatric emergence delirium is an undesirable outcome that is understudied.
Development of a predictive model is an initial step toward reducing its occurrence. This …

Our study forecasts the likelihood of firms resuming growth after periods of stagnation or
declining sales. We employ machine learning methods, including Random Forest …

Ovarian cancer (OC) is the most lethal of all gynecological cancers. Due to vague symptoms,
OC is mostly detected at advanced stages, with a 5-year survival rate (SR) of only 30%; …

Early identification of patients at high risk of delirium is crucial for its prevention. Our study
aimed to develop machine learning models to predict delirium after cardiac surgery using …

We aimed to develop, train, and validate machine learning models for predicting preterm
birth (< 37 weeks' gestation) in singleton pregnancies at different gestational intervals …

Objective Prognostic nutritional index (PNI) is an indicator to evaluate the nutritional immune
status of patients. This study aimed to assess whether preoperative PNI could predict the …