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Selected Topics in Diagnostic Studie...

Hua, Jia.

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Selected Topics in Diagnostic Studies, Biomarker Evaluation and Beyond.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Selected Topics in Diagnostic Studies, Biomarker Evaluation and Beyond./
作者:	Hua, Jia.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	207 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
Contained By:	Dissertations Abstracts International81-12B.
標題:	Public health. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27997476
ISBN:	9798641017006

Selected Topics in Diagnostic Studies, Biomarker Evaluation and Beyond.
Hua, Jia.

Selected Topics in Diagnostic Studies, Biomarker Evaluation and Beyond. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 207 p.

Source: Dissertations Abstracts International, Volume: 81-12, Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2020.

This item must not be sold to any third party vendors.

In the simplest setting of diagnostic tests, the outcomes are dichotomous, either healthy or diseased. For many diseases such as Alzheimer's disease, there usually exist one or more intermediate stages between healthy and fully diseased stages. Therefore, accurate multi-category classification and biomarker evaluation under such settings are of paramount importance for biomedical and clinical research. This thesis mainly aims to develop novel and optimal classification methods as well as new biomarker combination methods in multi-class setting. These methods are expected to have broad applicability in practice. Several machine learning (ML) methods have gained their popularity in a variety of fields; however, little connection has been built and inspected between machine learning methods and statistical methods for biomarker evaluation. This thesis work also aims to fill such gap. Specifically, this thesis work consists of three parts: 1) to present several unexploited methods for cut-point selection which are strong competitors against existing ones; 2) to propose several biomarker combination methods which are more efficient and superior to the existing ones; and finally 3) to compare some popular machine learning methods with proposed biomarker combination methods in binary classification setting.

ISBN: 9798641017006Subjects--Topical Terms:

534748
Public health.
Subjects--Index Terms:

AUC

Selected Topics in Diagnostic Studies, Biomarker Evaluation and Beyond.
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245 1 0 $a Selected Topics in Diagnostic Studies, Biomarker Evaluation and Beyond.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 207 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
500 $a Advisor: Tian, Lili.
502 $a Thesis (Ph.D.)--State University of New York at Buffalo, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a In the simplest setting of diagnostic tests, the outcomes are dichotomous, either healthy or diseased. For many diseases such as Alzheimer's disease, there usually exist one or more intermediate stages between healthy and fully diseased stages. Therefore, accurate multi-category classification and biomarker evaluation under such settings are of paramount importance for biomedical and clinical research. This thesis mainly aims to develop novel and optimal classification methods as well as new biomarker combination methods in multi-class setting. These methods are expected to have broad applicability in practice. Several machine learning (ML) methods have gained their popularity in a variety of fields; however, little connection has been built and inspected between machine learning methods and statistical methods for biomarker evaluation. This thesis work also aims to fill such gap. Specifically, this thesis work consists of three parts: 1) to present several unexploited methods for cut-point selection which are strong competitors against existing ones; 2) to propose several biomarker combination methods which are more efficient and superior to the existing ones; and finally 3) to compare some popular machine learning methods with proposed biomarker combination methods in binary classification setting.
590 $a School code: 0656.
650 4 $a Public health. $3 534748
650 4 $a Biostatistics. $3 1002712
653 $a AUC
653 $a Biomarker evaluation
653 $a Diagnostic studies
653 $a Generalized Youden index
653 $a Linear combination
653 $a ROC
690 $a 0573
690 $a 0308
710 2 $a State University of New York at Buffalo. $b Biostatistics. $3 2099967
773 0 $t Dissertations Abstracts International $g 81-12B.
790 $a 0656
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27997476