Predicting Demand For Emergency Ambulance Services: A Comparative Approach

Tanissara Butsingkorn ； Arthit Apichottanakul ； Sirawadee Arunyanart

《淡江理工學刊》 27卷10期 (2024/10) Pp. 3383-3388

https://doi.org/10.6180/jase.202410_27(10).0011

摘要

Accurate forecasting of demand for emergency medical services (EMS) is crucial for effective healthcare management, contributing to improved response times and cost control during emergencies. Additionally, it facilitates resource allocation and the implementation of knowledge-based policies, ultimately enhancing patient care and services. This study focuses on forecasting EMS demand related to patient transportation from 25 sub-hospitals in Khon Kaen, Thailand, to the central medical center hospital for the purpose of receiving necessary medical treatment. To improve the precision of demand forecasting, we evaluated various forecasting approaches. The results indicate that ANN outperforms other models. This can be attributed to the ANN's ability to identify complex relationships and efficiently learn from observed data through nonlinear mapping. These findings underscore the potential applications of the ANN model for addressing this problem.

關鍵字

Machine learning ； artificial neural networks ； autoregressive integrated moving averages ； simple moving averages ； nonlinear autoregressive ； wavelet nonlinear autoregressive