• Login
    View Item 
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Essays on Digital Health and Preventive Care Analytics

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    azu_etd_17126_sip1_m.pdf
    Size:
    3.861Mb
    Format:
    PDF
    Download
    Author
    Srinivasan, Karthik
    Issue Date
    2019
    Keywords
    blockwise missing patterns
    business analytics
    digital health
    network analysis
    preventive care
    wellbeing modeling
    Advisor
    Ram, Sudha
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Analytics is the discovery, interpretation, and communication of meaningful patterns in data. Analytics is an integral component of health information systems (IS), showing promise in various areas such as disease risk modeling, clinical intelligence, pharmacovigilance, precision medicine, hospitalization process optimization, digital health, preventive care, etc. In my dissertation, I focus on analytics in two important application areas of health IS, namely digital health and preventive care. Digital health analytics focuses on enhancing individual wellbeing via continuous tracking of health indicators, while preventive care analytics is the science of extracting insights from electronic health records to assist clinical decision-making towards preventing illness or diseases. With rapid development in healthcare big data and IoT technologies, research in digital health and preventive care (DHPC) analytics is increasing in importance and complexity. Limited predictors, incomplete data, non-linear input-outcome relationships, the presence of multiple outcomes, and heterogeneity in effects are some of the key challenges in DHPC analytics. My dissertation consists of three essays that introduces a collection of novel quantitative methods to address these challenges. The first essay presents a new feature engineering method that uses network science to predict high-cost patients at the point of admission in hospitals with limited information. The second essay describes a novel method to analyze incomplete data containing block-wise missing patterns using a reduced modeling approach. The third essay leverages a wearable devices-based study and introduces three new quantitative methods to model the effects of sound level on an individual’s physiological wellbeing in the workplace. The set of predictive and explanatory modeling methods proposed in these essays not only address important modeling challenges in DHPC analytics, but also more broadly contribute to business analytics, design science, and health IS research.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Management Information Systems
    Degree Grantor
    University of Arizona
    Collections
    Dissertations

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.