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Semi-supervised transductive regression for survival analysis in medical prognostics

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TitleInfo
Title
Semi-supervised transductive regression for survival analysis in medical prognostics
Name (type = personal)
NamePart (type = family)
Khan
NamePart (type = given)
Faisal M.
NamePart (type = date)
1981-
DisplayForm
Faisal M. Khan
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
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Kulikowski
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Casimir A
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Casimir A Kulikowski
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Advisory Committee
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chair
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NamePart (type = family)
Chen
NamePart (type = given)
Kevin
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Kevin Chen
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Michmizos
NamePart (type = given)
Konstantinos
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Konstantinos Michmizos
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Mitsis
NamePart (type = given)
Georgios
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Georgios Mitsis
Affiliation
Advisory Committee
Role
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outside member
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Rutgers University
Role
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degree grantor
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Graduate School - New Brunswick
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school
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Text
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theses
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2016
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2016-10
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2016
Place
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xx
Language
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eng
Abstract (type = abstract)
The central challenge in predictive modeling for survival analysis in medical prognostics is the management of censored observations in the data. While time-to-event predictions can be modeled as regression problems, traditional regression techniques are challenged by the censored characteristics of the data. In such problems the true target times of a majority of instances are unknown; what is known is a censored target representing some indeterminate time before the true target time. The information for most patients is incomplete and only known “up-to-a-point.” Patients who have experienced the endpoint of interest (cancer recurrence, death, etc) during an often multi-year study are considered as non-censored or events. They may represent as little as 9% of the available sample. Most of the patients do not experience the endpoint or are lost to follow-up for various reasons (patient moved, died of other causes, etc.). These censored samples often represent most of the available sample. Modeling techniques which can correctly account for censored observations are crucial. Such censored samples can be considered as semi-supervised targets, however most efforts in semi-supervised regression do not take into account the partial nature of unsupervised information; with samples treated as either fully labelled or unlabeled. This dissertation presents a novel transduction approach for semi-supervised survival analysis. The true target times are approximated from the censored times through transduction to improve predictive performance. The framework can be employed to transform traditional regression methods for survival analysis, or to enhance existing survival analysis algorithms for improved predictive performance. This proposed approach represents one of the first applications of semi-supervised regression to survival analysis and yields significant improvements in predictive performance for multiple applications in prostate and breast cancer prognostics.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Survival analysis (Biometry)
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
Identifier
ETD_7522
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xiv, 91 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Faisal M. Khan
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3474D5G
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Khan
GivenName
Faisal
MiddleName
M.
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-08-30 20:21:38
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Name
Faisal Khan
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2018-10-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 31st, 2018.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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