The Lancet

Machine learning prediction of 1-year mortality in older patients with heart failure: a nationwide, multicenter, prospective cohort study

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
Open Heart

Bridging machine learning and clinical practice: a multicentre nomogram for 90-day graft failure risk stratification in heart transplantation

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...
IEEE

Beyond the Ground Truth, XGBoost Model Applied to Sleep Spindle Event Detection

Abstract: Sleep spindles are microevents of the electroencephalogram (EEG) during sleep whose functional interpretation is not fully clear. To streamline the identification process and make it more ...
Frontiers

Machine learning prediction of ARDS after heart valve surgery: development and validation in Northwest China

Objective: To develop an AI-based predictive model for acute respiratory distress syndrome (ARDS) following cardiopulmonary bypass (CPB)-assisted heart valve replacement (HVR) to enable early ...
IEEE

Fault Diagnosis for Transformer Winding using Morpho-offset Feature of Oscillating Wave and CPO-XGBoost Algorithm

Abstract: Accurate and effective diagnosis of transformer winding conditions is crucial for ensuring their safe and reliable operation. This paper proposes a transformer winding fault detection method ...