A GIS-based Demand Forecast using Machine Learning for Emergency Medical Services
Date Issued
2014
Date
2014
Author(s)
Lu, Tsung-Yu
Abstract
The objective for Emergency Medical Services (EMSs) is to deliver patients at the right place and time with the shortest response time. By increasing the operational efficiency, the survival rate of patients could potentially be increased. The Geographic Information System (GIS) is introduced to manage and visualize the spatial distribution of training data and forecasting results. A flexible model is implemented in GIS, through which training data are prepared with user desired sizes for spatial grid and temporal steps. The authors applied Moving Average, Artificial Neural Network, Regression, and Support Vector Regression for the forecasting of pre-hospital emergency medical demand. The results from these approaches, as a reference, could be used for the pre-allocation of ambulances. A case study is conducted for the EMS in New Taipei City, where pre-hospital EMS data has been collected for 3 years. With the easy use of the model and acceptable prediction performance, the proposed approach has been shown to have its potential to be applied to the current practice.
Subjects
緊急救護服務
需求預測
機器學習
類神經網路
支持向量機
地理資訊系統
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-103-R01521529-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):91ae7e10d5e17dc38a6e83fc688a38da