• Login
    View Item 
    •   Shocker Open Access Repository Home
    • Engineering
    • Industrial, Systems, and Manufacturing Engineering
    • ISME Faculty Scholarship
    • ISME Research Publications
    • View Item
    •   Shocker Open Access Repository Home
    • Engineering
    • Industrial, Systems, and Manufacturing Engineering
    • ISME Faculty Scholarship
    • ISME Research Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival

    Date
    2016-06
    Author
    Dag, Ali
    Topuz, Kazim
    Oztekin, Asil
    Bulur, Serkan
    Megahed, Fadel M.
    Metadata
    Show full item record
    Citation
    Dag, Ali; Topuz, Kazim; Oztekin, Asil; Bulur, Serkan; Megahed, Fadel M. 2016. A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival. Decision Support Systems, vol. 86:pp 1–12
    Abstract
    Recent research has shown that data mining models can accurately predict the outcome of a heart transplant based on predictors that include patient and donor's health/demographics. These models have not been adopted in practice, however, since they did not: a) consider the interactions between the explanatory variables; b) provide a patient's specific risk of survival (reported results have been primarily deterministic); and c) offer an automated decision tool that can provide some data-driven insights to practitioners. In this study, we attempt to overcome these three limitations through the use of Bayesian Belief Networks (BBN). The proposed BBN framework is comprised of four phases. In the first two phases, the data is preprocessed, and a candidate set of predictors is generated based on employing several variable selection methods. The third phase involves the addition of medically relevant variables to the list. In phase four, the BBN model is applied. The results show that the proposed BBN method provides similar predictive performance to the best approaches in the-literature. More importantly, our method provides novel information on the interactions among the predictors and the conditional probability of survival for a given set of relevant donor recipient characteristics. We offer U.S. practitioners a decision support tool that presents an individualized survival score based on our BBN model (and the UNOS dataset).
    Description
    Click on the DOI link to access the article (may not be free).
    URI
    http://dx.doi.org/10.1016/j.dss.2016.02.007
    http://hdl.handle.net/10057/12139
    Collections
    • ISME Research Publications

    Browse

    All of Shocker Open Access RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace software copyright © 2002-2023  DuraSpace
    DSpace Express is a service operated by 
    Atmire NV