Data for: Combining Time Series and Recurrent Network Approaches for Long-Term Prediction of Emergency Department Admissions

Admissions to emergency departments exhibit high variability. This complicates the adjustment of resource allocation, resulting in rigid policies that lead to inefficient service. A number of studies have approached this issue by employing statistical analysis, machine learning regressors, or time series data models. We propose the use of improved recurrent neural networks that take into account the dynamic nature of the data and experience, introducing contextual variables that lead to better predictability. This allows the novelty of formulating a solution to a requirement that hospital managers have expressed in regard to the planning of available resources, a need that has thus far gone unmet: the availability of long prediction horizons for the purpose of long-term resource allocation. The present dataset contains data obtained from a medical facility. The formulation of the problem and detailed description of the dataset can be found in the article "Combining Time Series and Recurrent Network Approaches for Long-Term Prediction of Emergency Department Admissions".