ABSTRACTOvercrowding 0.859) than the decision tree (accuracy

ABSTRACTOvercrowding within emergency departments (EDs) can have significant negative consequences for patients. Therefore there is a need to explore the use of innovative methods to improve patient flow and prevent overcrowding. One of the possible methods is the use of data mining using machine learning techniques to predict ED admissions.

This study uses routinely collected administrative data (120,600 records) from two major acute hospitals in Northern Ireland to compare contrasting machine learning algorithms in predicting the risk of admission from the ED. We use three algorithms to build the predictive models: 1) logistic regression, 2) decision trees, and 3) gradient boosted machines (GBM).The GBM performed better (accuracy 80.31%, AUC-ROC 0.859) than the decision tree (accuracy 80.06%, AUC-ROC 0.

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824) and the logistic regression model (accuracy 79.94%, AUC-ROC 0.849). Drawing on logistic regression, we identify several factors related to hospital admissions, including hospital site, age, arrival mode, triage category, care group, previous admission in the past month, and previous admission in the past year. This study highlights the potential utility of three common machine learning algorithms in predicting patient admissions.

Practical implementation of the models developed in this study in decision support tools would provide a snapshot of predicted admissions from the ED at a given time, allowing for advance resource planning and the avoidance bottlenecks in patient flow, as well as comparison of predicted and actual admission rates. When interpretability is a key consideration, EDs should consider adopting logistic regression models, although GBM’s will be useful where accuracy is paramount.


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