A
Ali Dag
Researcher at Creighton University
Publications - 21
Citations - 487
Ali Dag is an academic researcher from Creighton University. The author has contributed to research in topics: Decision support system & Bayesian network. The author has an hindex of 8, co-authored 21 publications receiving 307 citations. Previous affiliations of Ali Dag include Auburn University & University of South Dakota.
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Predicting graft survival among kidney transplant recipients: A Bayesian decision support model
TL;DR: This study offers a novel methodological solution to this prediction problem by analyzing the retrospective database including > 31,000 U.S. patients and introducing a comprehensive feature selection framework that accounts for medical literature, data analytics methods and elastic net (EN) regression.
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Predicting heart transplantation outcomes through data analytics
TL;DR: A data-driven approach for predicting survival outcomes at multiple time-points is developed and successfully predicted short-, mid-, & long-term heart transplantation outcomes.
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Measuring the efficiency of hospitals: a fully-ranking DEA–FAHP approach
TL;DR: A DEA-based fuzzy multi-criteria decision making model for firms in the health care industry in order to enhance their business performance and the ability to make the most appropriate decision considering the value of the weights determined by the data from the hybrid model is presented.
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A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival
TL;DR: The results show that the proposed BBN method provides similar predictive performance to the best approaches in the literature and provides novel information on the interactions among the predictors and the conditional probability of survival for a given set of relevant donor-recipient characteristics.
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A hybrid data mining approach for identifying the temporal effects of variables associated with breast cancer survival
TL;DR: The study findings indicate that extremely parsimonious models can be developed by adopting a purely data-driven approach, rather than eliminating the variables manually.