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Predicting population health focused outcomes using machine learning
Descriptive
TitleInfo
Title
Predicting population health focused outcomes using machine learning
Name
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Arnold
NamePart
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David
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David Arnold
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author
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Srinivasan
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Shankar
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Shankar Srinivasan
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Advisory Committee
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chair
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Coffman
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Fredrick
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Fredrick Coffman
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Advisory Committee
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internal member
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Gohel
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Suril
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Suril Gohel
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Advisory Committee
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internal member
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Rutgers University
Role
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degree grantor
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School of Health Professions
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school
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Text
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theses
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2019
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2019-05
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2019
Language
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English
Abstract
Care management activities seek to reduce healthcare cost and improve patient outcomes. Identifying patients who may receive substantial benefit from care management services can be especially challenging when managing large populations across disparate systems. This research tests a novel method for identifying patients for care management using over 30 disparate healthcare data sources and machine learning. Random Forest models were used to predict four binary outcomes; high cost, hospital admission, hospital readmission, and multiple emergency department visits. The models leveraged population health enterprise data warehouse cross-ontology mappings for the following data types; conditions, procedures, medications, results, demographics, and claims-based cost and utilization. Each of the data types were tested independently then combined incrementally. The highest performing models for each outcome of interest resulted with the following ROC AUC; High Cost (0.81), Admission (0.80), Re-admission (0.86), and Multi-ED (0.74). The research shows disparate data sources and machine learning can be used to predict population health focused outcomes. The framework used in this research has the potential to expand and scale to include any number of additional data types and outcomes.
Subject
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Topic
Population health
Subject
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Topic
Biomedical Informatics
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
Identifier
ETD_9724
PhysicalDescription
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application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xvi, 104 pages) : illustrations
Note
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Ph.D.
Note
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Includes bibliographical references
Subject
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Topic
Machine learning
Subject
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Topic
Health services administration
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Title
School of Health Professions ETD Collection
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rucore10007400001
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(displayLabel = Rutgers, The State University of New Jersey)
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Identifier
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doi:10.7282/t3-9ck3-9485
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Arnold
GivenName
David
Role
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Permission or license
DateTime
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2019-04-08 21:06:55
AssociatedEntity
Name
David Arnold
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Copyright holder
Affiliation
Rutgers University. School of Health Professions
AssociatedObject
Type
License
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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
RULTechMD
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2019-04-11T20:29:18
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2019-04-11T20:29:18
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