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

START2
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.



machine learning techniques
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. 
Bayesian networks
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. 
decision trees
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 results were compared with the Sydney Triage to Admission Risk Tool (START), which uses logistic regression and six manually selected features.


 Machine learning methods demonstrate similar performance to logistic regression for ED disposition prediction models using basic triage information. 
logistic regression
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. 
naïve Bayes
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. 
neural networks
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.  Under the broad representation of presenting problem, the nearest neighbour algorithm with manual feature selection had the best AUC of 0.8206 (95% CI ±0.0006), while the decision tree with no feature selection had the best accuracy of 74.83% (95% CI ±0.065).  Under the specific representation, almost all models improved; the nearest neighbour with information gain feature selection had the best AUC of 0.8267 (95% CI ±0.0006), while the decision tree with wrapper or no feature selection had the best accuracy of 75.24% (95% CI ±0.064). 
nearest neighbour
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.  Under the specific representation, almost all models improved; the nearest neighbour with information gain feature selection had the best AUC of 0.8267 (95% CI ±0.0006), while the decision tree with wrapper or no feature selection had the best accuracy of 75.24% (95% CI ±0.064). 
wrapper