Study objective Machine learning models carry unique potential as decision-making aids
and prediction tools for improving patient care. Traumatically injured patients provide a …

To evaluate the performance of machine learning (ML) models and to compare it with
logistic regression (LR) technique in predicting cognitive impairment related to post …

Background With this study we aimed to design validated outcome prediction models in
moderate and severe traumatic brain injury (TBI) using demographic, clinical, and …

Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health
Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving …

Artificial intelligence, especially deep learning, is already being used in various fields
through the labeling of big data, as well as significant enhancements in computing power …

Background and objectives The acute physiology and chronic health evaluation-IV model
(APACHE-IV), and the sequential organ failure assessment (SOFA) score are two traditional …

Predicting poor neurological outcomes after resuscitation is important for planning treatment
strategies. We constructed an explainable artificial intelligence-based prognostic model …

Background Trauma is one of the most critical public health issues worldwide, leading to
death and disability and influencing all age groups. Therefore, there is great interest in …

Background: Mechanically ventilated patients in the intensive care unit (ICU) have high
mortality rates. There are multiple prediction scores, such as the Simplified Acute Physiology …

Background Rapid identification of high-risk polytrauma patients is crucial for early
intervention and improved outcomes. This study aimed to develop and validate machine …