Using Machine Learning Techniques to Support the Emergency Department
Keywords:Machine learning, emergency room, acute healthcare, random forest, hospitalization, diagnosis prediction
This research lays down foundations for a stronger presence of machine learning in the emergency department. Using machine learning to make predictions on a patient's situation can increase patient's health and decrease the waiting time. This paper explores to what extent it is possible to accurately predict ER outcome. These predictions will be based on routinely available ER data from a Dutch hospital. The data set used is representative for any Dutch Hospital. Prediction performance is compared between ML predictors. Using random forest and stacked ensemble gathered the best results. This research found that for more than half of the adult patients, the algorithm can very accurately predict hospitalization, with similar results for children and during the COVID-19. Moreover, it is investigated which characteristics and events contribute to the direction of the patient. Finally, several plans are introduced to substantially improve the ER process, for example by quickly reviewing patients selected by the algorithms. These might lead to an ER process that is significantly quicker, with more accurate diagnosis.