Revised Inverse Problem Algorithm-based Prediction of Carotid Stenosis from Carotid Artery Occlusion Patients' Clinical Data
碩士 === 中臺科技大學 === 醫學影像暨放射科學系暨研究所 === 106 === The clinical data of patients with carotid stenosis syndrome were analyzed using the revised inverse problem to predict the degree of stenosis in this study. Six factors defined as Age, Low-Density Cholesterol (LDL-C), Mean Arterial Pressure (MAP), Sugar...
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ndltd-TW-106CTC007700192019-05-30T03:50:41Z http://ndltd.ncl.edu.tw/handle/aacrx4 Revised Inverse Problem Algorithm-based Prediction of Carotid Stenosis from Carotid Artery Occlusion Patients' Clinical Data 利用逆運算疊代法評估頸動脈疾病患者之臨床數據預測其血管狹窄程度 DING, ING-CHI 丁嫈綺 碩士 中臺科技大學 醫學影像暨放射科學系暨研究所 106 The clinical data of patients with carotid stenosis syndrome were analyzed using the revised inverse problem to predict the degree of stenosis in this study. Six factors defined as Age, Low-Density Cholesterol (LDL-C), Mean Arterial Pressure (MAP), Sugar AC before feast and C-Reactive Protein (CRP) were adopted as high risk parameter to develop a first-order nonlinear semi-empirical formula with 16 items according to a revised inverse problem run by STATISTICA 7.0 software. Accordingly, six of high risk clinical data of 217 patients with carotid artery syndrome were collected and normalized to a range within -1~+1, respectively. This was essential to ensure that the theoretical calculation derived from each high risk factor was judged and evaluated under same criteria and no intrinsic bias was accompanied in the prediction. The results showed a high convincible prediction with final loss function value Φ 2.354, determination coefficient R2 0.935, and variance 87.46%. Furthermore, another group of 55 patients with same carotid artery syndrome were recruited and verified to ascertain the accuracy, and the obtained results showed a high degree of agreement, R2 0.875 as well. The major contribution of the prediction came from the CRP, since its coefficient was 0.7204 in the 16 items semi-empirical formula, thus CRP is the most dominant of the high risk factor among all six factors, whereas Age is the most minor factor for its smallest coefficient, 0.0256 as well. The theoretical prediction was accomplished according to the six high risk factors and provided helpful information for authorities to create appropriate policy for concerning the issue of public health care. Keywords:Carotid artery stenosis, Revised inverse problem, C-Reactive protein (CRP), Normalize, Brain ischemic stroke. LIU, BAI-SHUAN 劉百栓 2018 學位論文 ; thesis 77 zh-TW |
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碩士 === 中臺科技大學 === 醫學影像暨放射科學系暨研究所 === 106 === The clinical data of patients with carotid stenosis syndrome were analyzed using the revised inverse problem to predict the degree of stenosis in this study. Six factors defined as Age, Low-Density Cholesterol (LDL-C), Mean Arterial Pressure (MAP), Sugar AC before feast and C-Reactive Protein (CRP) were adopted as high risk parameter to develop a first-order nonlinear semi-empirical formula with 16 items according to a revised inverse problem run by STATISTICA 7.0 software. Accordingly, six of high risk clinical data of 217 patients with carotid artery syndrome were collected and normalized to a range within -1~+1, respectively. This was essential to ensure that the theoretical calculation derived from each high risk factor was judged and evaluated under same criteria and no intrinsic bias was accompanied in the prediction. The results showed a high convincible prediction with final loss function value Φ 2.354, determination coefficient R2 0.935, and variance 87.46%. Furthermore, another group of 55 patients with same carotid artery syndrome were recruited and verified to ascertain the accuracy, and the obtained results showed a high degree of agreement, R2 0.875 as well. The major contribution of the prediction came from the CRP, since its coefficient was 0.7204 in the 16 items semi-empirical formula, thus CRP is the most dominant of the high risk factor among all six factors, whereas Age is the most minor factor for its smallest coefficient, 0.0256 as well. The theoretical prediction was accomplished according to the six high risk factors and provided helpful information for authorities to create appropriate policy for concerning the issue of public health care.
Keywords:Carotid artery stenosis, Revised inverse problem, C-Reactive protein (CRP), Normalize, Brain ischemic stroke.
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author2 |
LIU, BAI-SHUAN |
author_facet |
LIU, BAI-SHUAN DING, ING-CHI 丁嫈綺 |
author |
DING, ING-CHI 丁嫈綺 |
spellingShingle |
DING, ING-CHI 丁嫈綺 Revised Inverse Problem Algorithm-based Prediction of Carotid Stenosis from Carotid Artery Occlusion Patients' Clinical Data |
author_sort |
DING, ING-CHI |
title |
Revised Inverse Problem Algorithm-based Prediction of Carotid Stenosis from Carotid Artery Occlusion Patients' Clinical Data |
title_short |
Revised Inverse Problem Algorithm-based Prediction of Carotid Stenosis from Carotid Artery Occlusion Patients' Clinical Data |
title_full |
Revised Inverse Problem Algorithm-based Prediction of Carotid Stenosis from Carotid Artery Occlusion Patients' Clinical Data |
title_fullStr |
Revised Inverse Problem Algorithm-based Prediction of Carotid Stenosis from Carotid Artery Occlusion Patients' Clinical Data |
title_full_unstemmed |
Revised Inverse Problem Algorithm-based Prediction of Carotid Stenosis from Carotid Artery Occlusion Patients' Clinical Data |
title_sort |
revised inverse problem algorithm-based prediction of carotid stenosis from carotid artery occlusion patients' clinical data |
publishDate |
2018 |
url |
http://ndltd.ncl.edu.tw/handle/aacrx4 |
work_keys_str_mv |
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