AIMC Topic: Risk Factors

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Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.

Advances in therapy
INTRODUCTION: This study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-te...

Understanding risk factors for postoperative mortality in neonates based on explainable machine learning technology.

Journal of pediatric surgery
PURPOSE: We aimed to introduce an explainable machine learning technology to help clinicians understand the risk factors for neonatal postoperative mortality at different levels.

Applying Machine Learning Across Sites: External Validation of a Surgical Site Infection Detection Algorithm.

Journal of the American College of Surgeons
BACKGROUND: Surgical complications have tremendous consequences and costs. Complication detection is important for quality improvement, but traditional manual chart review is burdensome. Automated mechanisms are needed to make this more efficient. To...

The Use of Artificial Neural Networks to Determine In-Hospital Mortality After Coronary Artery Bypass Surgery.

Journal of cardiothoracic and vascular anesthesia
OBJECTIVES: The aim of this study was to present an artificial neural network (ANN) model for the accurate estimation of in-hospital mortality and to demonstrate the validity of the model with real data and a comparison with conventional multiple lin...

Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation.

Respiratory research
BACKGROUND: Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pn...

Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study.

PloS one
BACKGROUND: Healthcare associated infections (HAI) are a major burden for the healthcare system and associated with prolonged hospital stay, increased morbidity, mortality and costs. Healthcare associated urinary tract infections (HA-UTI) accounts fo...

Predicting Length of Stay of Coronary Artery Bypass Grafting Patients Using Machine Learning.

The Journal of surgical research
BACKGROUND: There is a growing need to identify which bits of information are most valuable for healthcare providers. The aim of this study was to search for the highest impact variables in predicting postsurgery length of stay (LOS) for patients who...