Background Trauma is the leading cause of death in individuals younger than 40 years.
There are many different models for predicting patient outcome following trauma. To our …

To date, there are no reviews on machine learning (ML) for predicting outcomes in trauma.
Consequently, it remains unclear as to how ML-based prediction models compare in the …

This text presents state-of-the-art methods for developing concepts appropriate for nursing. It
offers a wide array of approaches to concept development, ranging from the classic to the …

A common goal of many clinical research studies is the development of a reliable clinical
decision rule, which can be used to classify new patients into clinically-important categories …

Background: Up to 50% of patients with minor traumatic brain injury (mTBI) develop
postconcussion syndrome (PCS). A decision rule to stratify risk for PCS is needed. Objective …

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

In this research we have used decision tree induction algorithm on Hospital Surveillance
data to classify admitted patients according to their critical condition. Three class labels, low …

The study described in this paper concerns natural object modeling in the context of
uncertain, imprecise and inconsistent representation. We propose a fuzzy system which …

Severity scores have long been used for the classification or stratification of patients for
clinical care and in the evaluation of outcomes. Such scores find highest utility as a …

Improving health care quality requires the elimination of unnecessary variation in the care
process. Decision support applications already exist that can foster adherence to standards …