Every year, millions of patients use emergency department facilities across the nation. The resources of these facilities have to be managed properly—if there is a large influx of patients at any given time, the staff and the available rooms should be increased accordingly. The mismatch between resources and patient influx could lead to wasted money and suboptimal care.

In this context, we introduce our example modeling task —predicting discharge statuses for patients presenting to the emergency room. The discharge status refers to whether patients are admitted to the hospital or sent home. Usually, the more serious cases are admitted to the hospital. Therefore, we are attempting to predict the outcome of the ED visit early on in the patient stay.

With such a model, the workflow of the hospital could be greatly improved, as well as resource flow. Many previous academic studies have looked at this problem (for an example, see Cameron et al., 2015).

You may be wondering why we didn't pick a different modeling task, such as readmission modeling or predicting CHF exacerbations. For one thing, the publicly available clinical data is very limited. The dataset that we chose is an ED dataset; there are no publicly available inpatient datasets available that are free to download without registering. Nevertheless, the task that we have chosen will serve our purposes in demonstrating how predictive healthcare models can be built.