E
Eren Demir
Researcher at University of Hertfordshire
Publications - 46
Citations - 494
Eren Demir is an academic researcher from University of Hertfordshire. The author has contributed to research in topics: Decision support system & Health care. The author has an hindex of 11, co-authored 43 publications receiving 362 citations. Previous affiliations of Eren Demir include Boğaziçi University & University of Westminster.
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Leptosphaeria spp., phoma stem canker and potential spread of L. maculans on oilseed rape crops in China
Xiaoxian Zhang,Xiaoxian Zhang,Xiaoxian Zhang,Robin P. White,Eren Demir,Małgorzata Jędryczka,R. M. Lange,M. Islam,Zi-Qin Li,Yongju Huang,Avice Hall,G. Zhou,Z. Wang,X. Cai,P. Skelsey,Bruce D.L. Fitt +15 more
TL;DR: In China, the incidence of phoma stem canker observed in pre-harvest surveys from 2005 to 2012 was greater on winter oil seed rape in provinces in central China than on spring oilseed rape in north China.
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A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach
TL;DR: This work developed a model that linked each and every service and specialty including A&E, and outpatient and inpatient services, with the aim of developing a linear optimization model to estimate the required bed capacity and staff needs of a mid-size hospital in England.
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A decision support tool for predicting patients at risk of readmission : a comparison of classification trees, logistic regression, generalized additive models and multivariate adaptive regression splines
TL;DR: This is the peer reviewed version of the following article: Eren Demir, “Classification Trees, Logistic Regression, Generalized Additive Models, and Multivariate Adaptive Regression Splines” Decision Sciences, Vol 45(5): 849-880, October 2014, which has been published in final form at doi: 10.1111/deci.12094.
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A comprehensive modelling framework to forecast the demand for all hospital services
TL;DR: This study confirmed that the best demand estimates arise from different forecasting methods and forecasting periods (ie, one size does not fit all), and outperformed traditional time series forecasting methods for a number of specialties.
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Towards an evidence-based decision making healthcare system management: Modelling patient pathways to improve clinical outcomes
TL;DR: This model is very useful in detecting the most critical threshold at which multiple readmissions are more probable, and could be a valuable tool for clinicians, health care managers, and policy makers for informed decision making in the management of diseases.