The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making.

Objective: To further develop and refine an Emergency Department (ED) in-patient admission prediction model using machine learning techniques.

Methods: This was a retrospective analysis of state-wide ED data from New South Wales, Australia. Six classification algorithms (Bayesian networks, decision trees, logistic regression, naïve Bayes, neural networks and nearest neighbour) and five feature selection techniques (none, manual, correlation-based, information gain and wrapper) were examined.

The Sydney Triage to Admission Risk Tool (START): A prospective validation study.

Objective: The present study aims to prospectively validate the Sydney Triage to Admission Risk Tool (START) to predict ED disposition.

Methods: This was a prospective validation study at two metropolitan EDs in Sydney, Australia. Consecutive triage encounters were observed by a trained researcher and START scores calculated.