Using Back-Propagation Network to Predict Proper Cyclosporine Dosage in Patients After Kidney Transplantation
碩士 === 大同大學 === 資訊工程學系(所) === 96 === Artificial intelligence technology has been extensively used in various applications. It is also used as an auxiliary tool for medical policy decision making. The application of back-propagation network in this research builds the assortment model from the histor...
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ndltd-TW-096TTU053920242016-05-13T04:14:58Z http://ndltd.ncl.edu.tw/handle/82288594317129688731 Using Back-Propagation Network to Predict Proper Cyclosporine Dosage in Patients After Kidney Transplantation 以倒傳遞網路預測環孢靈在腎臟移植後患者之用量適當性 Tsun-Hsiung Chang 張俊雄 碩士 大同大學 資訊工程學系(所) 96 Artificial intelligence technology has been extensively used in various applications. It is also used as an auxiliary tool for medical policy decision making. The application of back-propagation network in this research builds the assortment model from the history of kidney transplant patients who took the cyclosporine. About 66.29% of patients can be correctly identified by doctor’s personal experience to differentiate the results from using cyclosporine, while 86.81% of correctness is achieved by the application of back-propagation neural network strategy. We hope the results could help the medical personnel master the effectiveness of cyclosporine and improve the drug safety, the quality of using medicine, and the survival rate of kidney transplant patients. Yo-Ping Huang 黃有評 2008 學位論文 ; thesis 74 zh-TW |
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碩士 === 大同大學 === 資訊工程學系(所) === 96 === Artificial intelligence technology has been extensively used in various applications. It is also used as an auxiliary tool for medical policy decision making. The application of back-propagation network in this research builds the assortment model from the history of kidney transplant patients who took the cyclosporine. About 66.29% of patients can be correctly identified by doctor’s personal experience to differentiate the results from using cyclosporine, while 86.81% of correctness is achieved by the application of back-propagation neural network strategy. We hope the results could help the medical personnel master the effectiveness of cyclosporine and improve the drug safety, the quality of using medicine, and the survival rate of kidney transplant patients.
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Yo-Ping Huang |
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Yo-Ping Huang Tsun-Hsiung Chang 張俊雄 |
author |
Tsun-Hsiung Chang 張俊雄 |
spellingShingle |
Tsun-Hsiung Chang 張俊雄 Using Back-Propagation Network to Predict Proper Cyclosporine Dosage in Patients After Kidney Transplantation |
author_sort |
Tsun-Hsiung Chang |
title |
Using Back-Propagation Network to Predict Proper Cyclosporine Dosage in Patients After Kidney Transplantation |
title_short |
Using Back-Propagation Network to Predict Proper Cyclosporine Dosage in Patients After Kidney Transplantation |
title_full |
Using Back-Propagation Network to Predict Proper Cyclosporine Dosage in Patients After Kidney Transplantation |
title_fullStr |
Using Back-Propagation Network to Predict Proper Cyclosporine Dosage in Patients After Kidney Transplantation |
title_full_unstemmed |
Using Back-Propagation Network to Predict Proper Cyclosporine Dosage in Patients After Kidney Transplantation |
title_sort |
using back-propagation network to predict proper cyclosporine dosage in patients after kidney transplantation |
publishDate |
2008 |
url |
http://ndltd.ncl.edu.tw/handle/82288594317129688731 |
work_keys_str_mv |
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