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An advanced decision analytic methodology for improving organ allocation policies in the U.S. : = Lung transplant case.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	An advanced decision analytic methodology for improving organ allocation policies in the U.S. :/
其他題名:	Lung transplant case.
作者:	Al-Ebbini, Lina Mohammad Khair Mousa.
面頁冊數:	1 online resource (137 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.
標題:	Biomedical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369319002

An advanced decision analytic methodology for improving organ allocation policies in the U.S. : = Lung transplant case.
Al-Ebbini, Lina Mohammad Khair Mousa.

An advanced decision analytic methodology for improving organ allocation policies in the U.S. :Lung transplant case. - 1 online resource (137 pages)

Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.

Thesis (Ph.D.)--University of Massachusetts Lowell, 2016.

Includes bibliographical references

This study aims to determine which potential recipients would receive a lung for transplantation when it becomes available in the U.S. and to investigate the key factors influencing the allocation technique in lung transplantation process. A real dataset from the United Network for Organ Sharing (UNOS) was used through this investigation. Firstly, a fuzzy lung allocation system (FLAS) was developed to deal with the vagueness and fuzziness of the decision making of the medical experts in order to achieve accurate lung allocation process in terms of transplant survival time and functional status after transplantation. The results were very promising in terms of both prediction accuracy (R 2 = 83.2% and overall accuracy = 82.1%) and interpretation capabilities and hence' are superior to the existing techniques in literature. The limitation in that fuzzy approach resides in applying the commonly used patient/donor variables in the literature.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369319002Subjects--Topical Terms:

588770
Biomedical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

An advanced decision analytic methodology for improving organ allocation policies in the U.S. : = Lung transplant case.
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245 1 3 $a An advanced decision analytic methodology for improving organ allocation policies in the U.S. : $b Lung transplant case.
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500 $a Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.
500 $a Adviser: Asil Oztekin.
502 $a Thesis (Ph.D.)--University of Massachusetts Lowell, 2016.
504 $a Includes bibliographical references
520 $a This study aims to determine which potential recipients would receive a lung for transplantation when it becomes available in the U.S. and to investigate the key factors influencing the allocation technique in lung transplantation process. A real dataset from the United Network for Organ Sharing (UNOS) was used through this investigation. Firstly, a fuzzy lung allocation system (FLAS) was developed to deal with the vagueness and fuzziness of the decision making of the medical experts in order to achieve accurate lung allocation process in terms of transplant survival time and functional status after transplantation. The results were very promising in terms of both prediction accuracy (R 2 = 83.2% and overall accuracy = 82.1%) and interpretation capabilities and hence' are superior to the existing techniques in literature. The limitation in that fuzzy approach resides in applying the commonly used patient/donor variables in the literature.
520 $a Next, a hybrid methodology for feature selection was developed using genetic algorithms to identify such representative features (input variables) and thereby to ensure the development of the best possible analytic model to predict and explain the target variable, quality of life (QoL), for patients undergoing a lung transplant. The evaluation of three classification models, GA-kNN, GA-SVM, and GA-ANN, demonstrated that performance of the lung transplantation process has significantly improved via the GA-SVM approach, although the other two models have also yielded considerably high prediction accuracies.
520 $a In addition, we applied structural equation modeling (SEM) to investigate the relationship between the most descriptive recipients/donors features and lung transplant success. The developed framework of confirmatory factor analysis (CFA) examines the structure, reliability and validity of the lung allocation process. The proposed model recommends a new perspective of how medical experts and clinicians respond to the uncertain and challenging lung allocation strategy.
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538 $a Mode of access: World Wide Web
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710 2 $a University of Massachusetts Lowell. $3 1179049
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