Wichita State University

About: Wichita State University is a education organization based out in Wichita, Kansas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 4988 authors who have published 9563 publications receiving 253824 citations. The organization is also known as: WSU & Fairmount College.

Predicting graft survival among kidney transplant recipients: A Bayesian decision support model

Abstract: Predicting the graft survival for kidney transplantation is a high stakes undertaking considering the shortage of available organs and the utilization of healthcare resources. The strength of any predictive model depends on the selection of proper predictors. However, despite improvements in acute rejection management and short-term graft survival, the accurate prediction of kidney transplant outcomes remains suboptimal. Among other approaches, machine-learning techniques have the potential to offer solutions to this prediction problem in kidney transplantation. This study offers a novel methodological solution to this prediction problem by: (a) analyzing the retrospective database including > 31,000 U.S. patients; (b) introducing a comprehensive feature selection framework that accounts for medical literature, data analytics methods and elastic net (EN) regression (c) using sensitivity analyses and information fusion to evaluate and combine features from several machine learning approaches (i.e., support vector machines (SVM), artificial neural networks (ANN), and Bootstrap Forest (BF)); (d) constructing several different scenarios by merging different sets of features that are optioned through these fused data mining models and statistical models in addition to expert knowledge; and (e) using best performing sets in Bayesian belief network (BBN) algorithm to identify non-linear relationships and the interactions between explanatory factors and risk levels for kidney graft survival. The results showed that the predictor set obtained through fused data mining model and literature review outperformed the all other alternative predictors sets with the scores of 0.602, 0.684, 0.495 for F-Measure, Average Accuracy, and G-Mean, respectively. Overall, our findings provide novel insights about risk prediction that could potentially help in improving the outcome of kidney transplants. This methodology can also be applied to other similar transplant data sets.

Impact Damage Resistance and Tolerance of Honeycomb Core Sandwich Panels

Abstract: The damage resistance and tolerance of flat [(0/45),/core/(45/0), ] sandwich plates with honeycomb core subjected to low-velocity impacts using hemispherical steel impactors has been investigated experimentally. The effects of impactor diameter on the impact behavior, resulting impact damage states, and residual strength under in-plane compressive loading was of particular interest. The impact responses characterized in terms of peak impact force was observed to be dependent on the facesheet type, core thickness, and impactor size, but was found to be independent of the boundary support conditions. The smaller impactor produced damage states characterized by residual dent depths that were comparable to the core thickness, accompanied by visible facesheet fractures. The larger diameter impactor produced damage states with large core damage regions but with dent depths less than the facesheet thickness. Under in-plane compressive loading, depending on the impact damage state, contrasting failure mechanisms ...