Predicting emergency health care demands due to respiratory diseases

Abstract

Background: Timely care in the health sector is essential for the recovery of patients, and even more so in the case of a health emergency. In these cases, appropriate management of human and technical resources is essential. These are limited and must be mobilised in an optimal and efficient manner.

Objective: This paper analyses the use of the health emergency service in a city, Jaén, in the south of Spain. The study is focused on the most recurrent case in this service, respiratory diseases.

Methods: Machine Learning algorithms are used in which the input variables are multisource data and the target attribute is the prediction of the number of health emergency demands that will occur for a selected date. Health, social, economic, environmental, and geospatial data related to each of the emergency demands were integrated and related. Linear and nonlinear regression algorithms were used: support vector machine (SVM) with linear kernel and generated linear model (GLM), and the nonlinear SVM with Gaussian kernel.

Results: Predictive models of emergency demand due to respiratory disseases were generated with am absolute error better than 35 %.

Conclusions: This model helps to make decisions on the efficient sizing of emergency health resources to manage and respond in the shortest possible time to patients with respiratory diseases requiring urgent care in the city of Jaén.

Keywords: Geospatial data; Health Emergency Service; Machine Learning; Prediction.