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Reducing workflow model complexity for an automatic workflow discovery algorithm

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TitleInfo
Title
Reducing workflow model complexity for an automatic workflow discovery algorithm
Name (type = personal)
NamePart (type = family)
Tripathi
NamePart (type = given)
Juhi
NamePart (type = date)
1993-
DisplayForm
Juhi Tripathi
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Marsic
NamePart (type = given)
Ivan
DisplayForm
Ivan Marsic
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Striki
NamePart (type = given)
Maria
DisplayForm
Maria Striki
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Chen
NamePart (type = given)
Yingying
DisplayForm
Yingying Chen
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
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school
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Text
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theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
With the increasing number of adverse patient outcomes and deaths due to medical errors, process mining techniques have been used to build automatic workflow discovery algorithms to assist teams during the treatment procedure, better understand the treatment practice and potentially help improve patient outcomes. These automatic workflow discovery algorithms use event logs that has all the information of the events that are executed in the treatment procedure, to extract a process model. Automatic workflow discovery algorithms are now used to analyze the trauma resuscitation process and improvise the patient outcomes. The widespread use of process mining techniques to discover and analyze workflows in healthcare, has motivated this study that has analyzed, how varying a parameter that contributes to the workflow discovery algorithm changes the workflow model, discusses the reasons behind those changes in the discovered workflow, presents the optimum value of the parameter that produces best results and backs up the theoretical discussion with numerical results. We use an expert-based model that is derived from hand drawn model by medical experts after multiple revisions, that serves as the ground truth and to compare the accuracy of our workflow model. Based on the comparison of the workflow model with the expert model, the variations observed could be classified into the categories : an incorrect addition of treatment activity, a redundant addition of treatment activity or a correct addition of treatment activity to the workflow. This work is an attempt in the direction to make the workflow model generated using the workflow algorithm, more accurate and exhibits least complexity, thereby making it easy to comprehend. The workflow discovery algorithm used for this work has two phases : (1) Construction of an event sequence of consensus activities that has the occurrence probability of more than the predefined threshold. This occurrence probability is determined using the event logs. This phase ensures that the workflow model comprises of the frequent activities that have appeared in the event logs. (2) Inclusion of non-consensus activities, that are common but dispersed between consensus activities in the workflow. This inclusion is done after multiple iterations through consensus activities to determine the activities that are interleaved between consensus activities and when considering a window of consecutive consensus activities, the combined probability of occurrence of the non-consensus is more than the predefined threshold.

This study explores one of the contributing hyperparameters of the algorithm, to infer its effect on the workflow model. The analysis falls in agreement with the underlying notion behind this study, that varying the hyperparameter of workflow discovery algorithms results in varying complexity of the workflow model.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = LCSH)
Topic
Workflow
Subject (authority = LCSH)
Topic
Workflow management systems
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10244
PhysicalDescription
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InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (viii, 62 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-k1gz-s038
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Tripathi
GivenName
Juhi
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-14 16:00:18
AssociatedEntity
Name
Juhi Tripathi
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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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2019-09-16T20:08:22
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2019-09-16T20:08:22
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