In patients with major traumatic injuries, early intervention can be lifesaving. However,
identifying high-risk patients can be difficult, and judgement errors may compromise optimal …

There is poor uptake of prognostic decision support models by clinicians regardless of their
accuracy. There is evidence that this results from doubts about the basis of the model as the …

Bayesian networks have been widely proposed to assist clinical decision making. Their
popularity is due to their ability to combine different sources of information and reason under …

Developing clinical decision support systems (CDSS) that accurately capture the way
clinicians gather information and make decisions during patient's care is challenging. The …

Many prognostic models are not adopted in clinical practice regardless of their reported
accuracy. Doubts about the basis of the model is considered to be a major reason for this as …

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

Background We know from previous studies that increasing age, abnormal vital signs and
abnormal acid-base status are strongly associated with in-hospital mortality in unselected …

Background: Graphical probabilistic models have the ability to provide insights as to how
clinical factors are conditionally related. These models can be used to help us understand …

Uncontrolled post-traumatic hemorrhage is an important cause of traumatic mortality that can
be avoided. This study intends to use machine learning (ML) to build an algorithm based on …

With trauma being one of the leading causes of death in the US, automated decision support
systems that can accurately detect traumatic injuries and predict their outcomes are crucial …