Using machine learning techniques for predictionemergency department visits

碩士 === 健行科技大學 === 工業工程與管理系 === 105 === In the emergency room of the hospital, patients who need emergency medical care are waiting for emergency treatment, so the emergency room time every minute is very valuable. Because the health insurance system is very serious and the emergency medical resource...

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Main Authors: Jin-Rong Guo, 郭進榮
Other Authors: Chi-Jie Lu
Format: Others
Language:zh-TW
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/bee699
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spelling ndltd-TW-105CYU050410012019-05-16T00:15:14Z http://ndltd.ncl.edu.tw/handle/bee699 Using machine learning techniques for predictionemergency department visits 比較多種機器學習預測技術於急診看診人數預測之研究 Jin-Rong Guo 郭進榮 碩士 健行科技大學 工業工程與管理系 105 In the emergency room of the hospital, patients who need emergency medical care are waiting for emergency treatment, so the emergency room time every minute is very valuable. Because the health insurance system is very serious and the emergency medical resources are limited, if we can predict how many patients will appear in the emergency room, the hospital will be able to dispatch the resources more effectively, but also reduce the pressure of the medical staff in the emergency, improve the medical Quality, emergency patients waiting time will be reduced, so the emergency medical staff how to effectively allocate resources to provide patients with the best quality service is a question worthy of discussion. This study uses ten prediction modes such as Simple Linear Regression, Linear Regression, Multilayer Perceptron, SVR, RBF Network, IBK, K Star, LWL, REP Tree and M5 Rules in Weka system. The purpose of this paper is to compare the prediction techniques of machine learning Method to find out the forecasting technology suitable for predicting the number of emergencies, hoping to be the basis for the allocation of human resources and medical resources. The experimental results show that the SVR and REP Tree prediction models are more suitable for the prediction of the number of emergencies provided by the regional hospitals. Chi-Jie Lu 呂奇傑 2017 學位論文 ; thesis 66 zh-TW
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description 碩士 === 健行科技大學 === 工業工程與管理系 === 105 === In the emergency room of the hospital, patients who need emergency medical care are waiting for emergency treatment, so the emergency room time every minute is very valuable. Because the health insurance system is very serious and the emergency medical resources are limited, if we can predict how many patients will appear in the emergency room, the hospital will be able to dispatch the resources more effectively, but also reduce the pressure of the medical staff in the emergency, improve the medical Quality, emergency patients waiting time will be reduced, so the emergency medical staff how to effectively allocate resources to provide patients with the best quality service is a question worthy of discussion. This study uses ten prediction modes such as Simple Linear Regression, Linear Regression, Multilayer Perceptron, SVR, RBF Network, IBK, K Star, LWL, REP Tree and M5 Rules in Weka system. The purpose of this paper is to compare the prediction techniques of machine learning Method to find out the forecasting technology suitable for predicting the number of emergencies, hoping to be the basis for the allocation of human resources and medical resources. The experimental results show that the SVR and REP Tree prediction models are more suitable for the prediction of the number of emergencies provided by the regional hospitals.
author2 Chi-Jie Lu
author_facet Chi-Jie Lu
Jin-Rong Guo
郭進榮
author Jin-Rong Guo
郭進榮
spellingShingle Jin-Rong Guo
郭進榮
Using machine learning techniques for predictionemergency department visits
author_sort Jin-Rong Guo
title Using machine learning techniques for predictionemergency department visits
title_short Using machine learning techniques for predictionemergency department visits
title_full Using machine learning techniques for predictionemergency department visits
title_fullStr Using machine learning techniques for predictionemergency department visits
title_full_unstemmed Using machine learning techniques for predictionemergency department visits
title_sort using machine learning techniques for predictionemergency department visits
publishDate 2017
url http://ndltd.ncl.edu.tw/handle/bee699
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