Application of Back Propagation Neural Network on the Length of Stay Prediction for Cardiovascular Patients

碩士 === 國立臺北科技大學 === 工業工程與管理系碩士班 === 103 === The major concern for most hospitalists in Taiwan is to serve the aging population with the limited healthcare budget provided by the National Health Insurance Administration. As hospital beds are scarce, high occupancy rates are often preferred. To develo...

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Main Authors:	CHEN YEN-YU, 陳彥佑
Other Authors:	蔡佩芳
Language:	zh-TW
Published:	2015
Online Access:	http://ndltd.ncl.edu.tw/handle/bmkwv7

id	ndltd-TW-103TIT05031018
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spelling	ndltd-TW-103TIT050310182019-07-04T05:57:55Z http://ndltd.ncl.edu.tw/handle/bmkwv7 Application of Back Propagation Neural Network on the Length of Stay Prediction for Cardiovascular Patients 倒傳遞類神經網路應用於心血管疾病患者住院天數預測 CHEN YEN-YU 陳彥佑碩士國立臺北科技大學工業工程與管理系碩士班 103 The major concern for most hospitalists in Taiwan is to serve the aging population with the limited healthcare budget provided by the National Health Insurance Administration. As hospital beds are scarce, high occupancy rates are often preferred. To develop efficient admission policy and optimize bed management, it would be beneficial to investigate the critical factors which might determine the length of stay (LOS) in the early stage of admission. This research is to use artificial neural network (ANN) models to predict the LOS for patients in cardiology department during the pre-admission stage. After comparing with the regression model, the ANN models are able to predict patients with longer LOS. 蔡佩芳陳凱瀛 2015 學位論文 ; thesis zh-TW
collection	NDLTD
language	zh-TW
sources	NDLTD
description	碩士 === 國立臺北科技大學 === 工業工程與管理系碩士班 === 103 === The major concern for most hospitalists in Taiwan is to serve the aging population with the limited healthcare budget provided by the National Health Insurance Administration. As hospital beds are scarce, high occupancy rates are often preferred. To develop efficient admission policy and optimize bed management, it would be beneficial to investigate the critical factors which might determine the length of stay (LOS) in the early stage of admission. This research is to use artificial neural network (ANN) models to predict the LOS for patients in cardiology department during the pre-admission stage. After comparing with the regression model, the ANN models are able to predict patients with longer LOS.
author2	蔡佩芳
author_facet	蔡佩芳 CHEN YEN-YU 陳彥佑
author	CHEN YEN-YU 陳彥佑
spellingShingle	CHEN YEN-YU 陳彥佑 Application of Back Propagation Neural Network on the Length of Stay Prediction for Cardiovascular Patients
author_sort	CHEN YEN-YU
title	Application of Back Propagation Neural Network on the Length of Stay Prediction for Cardiovascular Patients
title_short	Application of Back Propagation Neural Network on the Length of Stay Prediction for Cardiovascular Patients
title_full	Application of Back Propagation Neural Network on the Length of Stay Prediction for Cardiovascular Patients
title_fullStr	Application of Back Propagation Neural Network on the Length of Stay Prediction for Cardiovascular Patients
title_full_unstemmed	Application of Back Propagation Neural Network on the Length of Stay Prediction for Cardiovascular Patients
title_sort	application of back propagation neural network on the length of stay prediction for cardiovascular patients
publishDate	2015
url	http://ndltd.ncl.edu.tw/handle/bmkwv7
work_keys_str_mv	AT chenyenyu applicationofbackpropagationneuralnetworkonthelengthofstaypredictionforcardiovascularpatients AT chényànyòu applicationofbackpropagationneuralnetworkonthelengthofstaypredictionforcardiovascularpatients AT chenyenyu dàochuándìlèishénjīngwǎnglùyīngyòngyúxīnxuèguǎnjíbìnghuànzhězhùyuàntiānshùyùcè AT chényànyòu dàochuándìlèishénjīngwǎnglùyīngyòngyúxīnxuèguǎnjíbìnghuànzhězhùyuàntiānshùyùcè
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Application of Back Propagation Neural Network on the Length of Stay Prediction for Cardiovascular Patients

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