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Development of an automated system for querying radiology reports and recording deep venous thromboses and pulmonary emboli

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TitleInfo
Title
Development of an automated system for querying radiology reports and recording deep venous thromboses and pulmonary emboli
Name (type = personal)
NamePart (type = family)
Narain
NamePart (type = given)
Wazim R.
NamePart (type = date)
1978-
DisplayForm
Wazim R. Narain
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Srinivasan
NamePart (type = given)
Shankar
DisplayForm
Shankar Srinivasan
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Coffman
NamePart (type = given)
Frederick
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Frederick Coffman
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Stetson
NamePart (type = given)
Pete
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Pete Stetson
Affiliation
Advisory Committee
Role
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outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Health Professions
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
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theses
OriginInfo
DateCreated (qualifier = exact)
2016
DateOther (qualifier = exact); (type = degree)
2016-05
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2016
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
As the United States healthcare system transitions to a pay for performance model in response to increasing costs and utilization, assessing quality of care has come to the forefront. Venous thromboembolisms (VTE), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), is a key measure of quality of hospital care and are associated with increased morbidity, mortality and cost in hospitalized patients. Traditional ways of measuring quality and identifying adverse events such as VTE using administrative data are convenient but lack accuracy. Manual review of clinical records is widely considered the gold standard but resource intensive. Consequently, this study sought to determine the accuracy of Natural Language Processing (NLP) and machine learning classifiers in identifying VTE from free text data. This study used radiology reports performed within 30 days of surgery for hospital patients sampled from 2011 through 2014 as part of the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP). Though records for this sample were previously reviewed and VTE cases identified, a total of 909 ultrasound reports and 1,837 computed tomography (CT) angiogram reports were again manually reviewed to identify DVT/PE within each report and served as the gold standard. The Naïve Bayes, k-Nearest Neighbors (kNN), C4.5 decision tree, and support vector machine (SVM) classifiers were trained on 70% of the total preprocessed reports and performance was assessed on the remaining 30%. DVTs were identified in 16.8% of all ultrasound reports and PEs were identified in 5.0% of all CT angiogram reports. SVM yielded the best results in classifying both DVT and PE, with precision of 91.3%, recall of 95.5% and F-measure of 93.3% for DVT classification and precision of 93.1%, recall of 87.1% and F-measure of 90.0% for PE classification. In conclusion, NLP along with statistical machine learning classifiers can accurately identify VTE from narrative radiology reports.
Subject (authority = RUETD)
Topic
Biomedical Informatics
Subject (authority = ETD-LCSH)
Topic
Pulmonary embolism
Subject (authority = ETD-LCSH)
Topic
Thrombosis
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
RelatedItem (type = host)
TitleInfo
Title
School of Health Professions ETD Collection
Identifier (type = local)
rucore10007400001
Identifier
ETD_7311
Identifier (type = doi)
doi:10.7282/T3T155RQ
PhysicalDescription
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (79 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Wazim R. Narain
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Narain
GivenName
Wazim
MiddleName
R.
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-04-22 14:00:52
AssociatedEntity
Name
Wazim Narain
Role
Copyright holder
Affiliation
Rutgers University. School of Health Related Professions
AssociatedObject
Type
License
Name
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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2016-04-27T12:45:46
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2016-04-27T12:45:46
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