Forecasting and explaining emergency department visits in a public hospital

Abstract

Emergency Departments (EDs) are the most overcrowded places in public hospitals. Machine learning can support decisions on effective ED resource management by accurately forecasting the number of ED visits. In addition, Explainable Artificial Intelligence (XAI) techniques can help explain decisions from forecasting models and address challenges like lack of trust in machine learning results. The objective of this paper is to use machine learning and XAI to forecast and explain the ED visits on the next on duty day. Towards this end, a case study is presented that uses the XGBoost algorithm to create a model that forecasts the number of patient visits to the ED of the University Hospital of Ioannina in Greece, based on historical data from patient visits, time-based data, dates of holidays and special events, and weather data. The SHapley Additive exPlanations (SHAP) framework is used to explain the model. The evaluation of the forecasting model resulted in an MAE value of 18.37, revealing a more accurate model than the baseline, with an MAE of 29.38. The number of patient visits is mostly affected by the day of the week of the on duty day, the mean number of visits in the previous four on duty days, and the maximum daily temperature. The results of this work can help policy makers in healthcare make more accurate and transparent decisions that increase the trust of people affected by them (e.g., medical staff).