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Precision networks and information retrieval for designing and analyzing clinical studies

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TitleInfo
Title
Precision networks and information retrieval for designing and analyzing clinical studies
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Small
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Ellie
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1962-
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Ellie Small
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author
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Cabrera
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Javier
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Javier Cabrera
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Advisory Committee
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chair
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David E
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David E Tyler
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Advisory Committee
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internal member
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Kolassa
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John E
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John E Kolassa
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Advisory Committee
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internal member
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Kostis
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John B
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John B Kostis
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Advisory Committee
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Rutgers University
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degree grantor
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School of Graduate Studies
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theses
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2019
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2019-05
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2019
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English
Abstract (type = abstract)
A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via one directed acyclic graph. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.
However, in some cases, the situation at hand does not lend itself to the single network model. Sometimes each observation represents a network, and so we are dealing with many networks rather than just one. We refer to these individual networks as precision networks. As an example, we may have a set of patients, each of which suffered multiple symptoms, conditions, and diseases referred to as events. These events may or may not be related to each other. A precision network, here called a precision disease network or PDN, may be created for each patient, and the total set of such PDNs can be stored and analyzed together.
In order to build such a PDN for each patient, we need to establish when events are related and when they are not. We developed a nonparametric algorithm that will determine whether such a relationship likely exists for two events, based on a data set with patients who experienced both. If such a relationship appears likely, we can provide an estimate of the proportion of dependent observations based on the time period between the two events. With the help of medical professionals, we may then establish an interval of time differences between those events within which we consider the events related, and outside of which we consider the events to be independent.
We note that medical researchers are often in need of finding new and interesting ideas for research within a topic. Those researchers will access the PubMed database and extract publications for the desired topic, usually resulting in a large amount of publications. They will then spend significant amounts of time perusing the abstracts of these publications in order to find an interesting idea that may be a candidate for a new clinical study.
We have developed a new method and computer application that examines all abstracts that fulfill the general search terms from bibliographic databases such as PubMed, mines those extracts for non-trivial, frequently occurring words, and allows for clustering of the abstracts using those words. By clustering and repeatedly re-clustering interesting clusters, a researcher can find an interesting subject for a new clinical study in a fraction of the time they spent previously.
We have also developed a new method to extract quality phrases from large volumes of text. Using this method, we have created an extension to the mining of abstracts that allows the clustering of quality phrases rather than words.
Subject (authority = local)
Topic
Statistics
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
Subject (authority = LCSH)
Topic
Clinical trials
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Rutgers University Electronic Theses and Dissertations
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ETD_9603
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application/pdf
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text/xml
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1 online resource (xii, 118 pages) : illustrations
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Ph.D.
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Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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Identifier (type = doi)
doi:10.7282/t3-hxm9-2f33
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
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Name
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Small
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Ellie
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Permission or license
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2019-04-01 12:27:12
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Name
Ellie Small
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Affiliation
Rutgers University. School of Graduate Studies
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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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2019-03-30T13:16:22
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2019-03-30T13:16:22
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