• Login
    View Item 
    •   UMB Digital Archive
    • School, Graduate
    • Theses and Dissertations All Schools
    • View Item
    •   UMB Digital Archive
    • School, Graduate
    • Theses and Dissertations All Schools
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UMB Digital ArchiveCommunitiesPublication DateAuthorsTitlesSubjectsThis CollectionPublication DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    Display statistics

    Applying Bayesian network approaches to study health outcomes

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Find Full text
    Author
    Lee, Sun-Mi
    Advisor
    Abbott, Patricia A., Ph.D., M.S.
    Date
    2003
    Type
    dissertation
    
    Metadata
    Show full item record
    Abstract
    Background. In today's healthcare environment, the proliferation of information systems has facilitated the growth of large clinical and administration databases. Innovative knowledge discovery approaches in large healthcare databases via data mining techniques have been actively used in the analysis of these data. Of particular interest are Bayesian networks, which have recently emerged as powerful data mining algorithms for pattern recognition and classification. Purpose. The purpose of this study was to explore the feasibility of using Bayesian networks (BN) in studying health outcomes. The specific aims were to develop a BN model to identify predictors of limited health service utilization in HIV positive persons and evaluate the model by comparing it to the predictive performance of Naive Bayes (NB) and logistic regression (LG) models. Methods. This study used the HIV Cost and Services Utilization Study dataset consisting of 2,864 HIV positive adults. A total of 36 variables including two service utilization variables (hospitalization and outpatient visits) were selected. HUGIN Researcher(TM) 6.3 was used to develop the BN and NB models; SAS/STAT PROC LOGISTIC was used to develop LG models. Results. The BN model successfully captured relationships explaining complex patterns of human behavior in health service utilization. The area under the receiver operating characteristic curve (AUC) measuring the BN model's discriminatory power when predicting hospitalization was .72 (CI: .70, .74). The AUC of the BN model was statistically higher than that of the NB model (.68; CI: .66, .70), but no higher than that of the LG model (.70; CI: .67, .72) using the 8 variables from a previous study by Shapiro and colleagues (1999b). In a second analysis using the 10 influential variables discovered by the BN approach, the NB and LG performance improved (NB: .74 (CI: .72,.76); LG: .74 (CI: .72, .75)).;Conclusion/implication. The BN approaches contributed to the discovery of the influential predictors that lead to an increase of the models' predictive performance. This study provided new insight in working with large healthcare databases. When attempting to discover unknown relationships that might be missed by traditional analysis methods alone, investigators should consider the use of BNs.
    Description
    University of Maryland, Baltimore. Nursing. Ph.D. 2003
    Keyword
    Health Sciences, Nursing
    Information Science
    Health Sciences, Health Care Management
    Computer Science
    Bayesian networks
    Bayes Theorem
    Health Information Systems
    Health Services--utilization
    HIV Infections
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/1126
    Collections
    Theses and Dissertations School of Nursing
    Theses and Dissertations All Schools

    entitlement

     
    DSpace software (copyright © 2002 - 2021)  DuraSpace
    Quick Guide | Policies | Contact Us | UMB Health Sciences & Human Services Library
    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.