Evaluating the impact of exogenous variables for patients forecasting in an Emergency Department using Attention Neural Networks

Abstract

Emergency Department overcrowding is a well-known problem. The consequences are long waiting times for patients, reduced service quality, and the potential for increased mortality rates. Moreover, this problem is escalating with the aging of the population and the chronification of diseases. Being aware of such concerns, Emergency Departments started to conduct patient accountability years ago, and now they are ready to take advantage of the gathered data to improve the quality of service to citizens. However, to achieve this goal, they need the technology that enables the generation of predictive models from such data to improve the planning of their resources. In this paper, we face the task of forecasting patient admissions by using Deep Neural Networks, particularly a modern Attention-based model. As well, historical records do not provide a full picture of how patients attend the emergency service. Relying on the possibilities given by the attention mechanism, we also propose the use of exogenous information such as calendar data, weather, air quality, allergens, and information extracted from the web via Google Trends to enhance admissions prediction accuracy. In this regard, we have tested different configurations of the exogenous variables to isolate which ones provide relevant information that improves the model. In our experiments, we have seen that the calendar, weather, and air quality provide the most valuable information, meanwhile, allergens and Google Trends data appear to be hindering the models’ performance.