Machine learning methods for predicting the admissions and hospitalisations in the emergency department of a civil and military hospital

Hospitals' Emergency Departments (ED) have a great relevance in the health of the population. Properly managing the ED department requires to optimise the service, while maintaining a high quality care. This trade-off implies to properly arrange the schedule for the personnel, so the service can duly attend all patients. In this regard, a key point is to know in advance how many patients will arrive to the service and the number that should be derived to hospitalisation. To provide such information, we present the results of applying different algorithms for forecasting ED admissions and hospitalisations for both seven days and four months ahead. To do this, we have employed the ED admissions and inpatients series from a Spanish civil and military hospital. The ED admissions have been aggregated on a daily basis and on the official workers' shifts, while the hospitalisations series have been considered daily. Over that data we employ two algorithms types: time series (AR, H-W, SARIMA and Prophet) and feature matrix (LR, EN, XGBoost and GLM). In addition, we create all possible ensembles among the models in order to find the best forecasting method. The findings of our study demonstrate that the ensembles can be beneficial in obtaining the best possible model.