Chen, A.Y.A.Y.ChenLu, T.-Y.T.-Y.LuMATTHEW HUEI-MING MAALBERT CHENWEI-ZEN SUN2020-12-242020-12-2420162168-2194https://scholars.lib.ntu.edu.tw/handle/123456789/532148The objective of prehospital emergency medical services (EMSs) is to have a short response time. By increasing the operational efficiency, the survival rate of patients could potentially be increased. The geographic information system (GIS) is introduced in this study to manage and visualize the spatial distribution of demand data and forecasting results. A flexible model is implemented in GIS, through which training data are prepared with user-desired sizes for the spatial grid and discretized temporal steps. We applied moving average, artificial neural network, sinusoidal regression, and support vector regression for the forecasting of prehospital emergency medical demand. The results from these approaches, as a reference, could be used for the preallocation of ambulances. A case study is conducted for the EMS in New Taipei City, where prehospital EMS data have been collected for three years. The model selection process has chosen different models with different input features for the forecast of different areas. The best daily mean absolute percentage error during testing of the EMS demand forecast is 23.01%, which is a reasonable forecast based on Lewis' definition. With the acceptable prediction performance, the proposed approach has its potential to be applied to the current practice. ? 2015 IEEE.[SDGs]SDG3Ambulances; Emergency services; Forecasting; Geographic information systems; Information use; Medical information systems; Neural networks; Support vector machines; Current practices; Demand forecast; Emergency medical services; Mean absolute percentage error; Operational efficiencies; Prediction performance; Short response time; Support vector regression (SVR); Information management; ambulance; artificial neural network; emergency health service; forecasting; geographic information system; human; prediction; support vector machine; biology; emergency health service; geographic information system; procedures; statistics and numerical data; support vector machine; utilization; Ambulances; Computational Biology; Emergency Medical Services; Forecasting; Geographic Information Systems; Humans; Neural Networks (Computer); Support Vector Machinejournal article10.1109/JBHI.2015.2443799260875072-s2.0-84978296219