Xiaojun "Gene" Shan
Permanent URI for this collectionhttps://hdl.handle.net/10657.1/1656
Dr. Xiaojun (Gene) Shan is an Assistant Professor of Engineering Management at University of Houston-Clear Lake. Dr. Shan's research interests are in the areas of Healthcare systems engineering, modeling, applied operations research/optimization, continuous process improvement, health information systems, data mining and big data analytics, with emphasis on operational excellence; Mathematical modeling (with focus on game-theoretic modeling) of complex systems (e.g., health care delivery, defense and electricity systems); Risk management against man-made and natural disasters.
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Item 30-day mortality prediction of patients with congestive heart failure (CHF)(Institute of Industrial and Systems Engineer (IISE) Annual Conference and Expo 2016, 2016-05-21) Khuriekar, N.; Aladeemy, M.; Chou, C. A.; Shan, Xiaojun; Poranki, S.; Khasawneh, M. T.; Srihari, K.Abstract not available.Item 30-Day Mortality Prediction of Patients With Congestive Heart Failure (CHF)(2016-05-21) Khuriekar, N.; Aladeemy, M.; Chou, C. A.; Shan, Xiaojun; Poranki, S.; Khasawneh, M. T.; Srihari, K.The paper builds a prediction model of 30-day mortality risk in patients with CHF. Least absolute shrinkage and selection operator (LASSO) was applied to select the significant features. Three data mining techniques, namely, decision tree, logistic regression, and AdaBoost.M1 algorithms were used to predict mortality risk in patients with CHF. A case study was conducted using data (January 2012 to December 2014) from a community hospital in Upstate New York and a comparison among the three predictive algorithms was performed. The primary measure for comparing the performance of the prediction algorithms were area under the receiver operating characteristic curve (AUC), specificity, sensitivity, and accuracy. The results show that logistic regression model resulted in an accuracy of 84.85% with the specificity, sensitivity and AUC of 1, 0.44 and 0.72, respectively. On the other hand, the decision tree algorithm resulted in an accuracy of 75.76% with specificity, sensitivity and AUC of 0.83, 0.56 and 0.69, respectively, whereas the AdaBoost.M1 algorithm resulted in 81.82% accuracy with specificity, sensitivity and AUC of 0.56, 0.54 and 0.55, respectively. This research concludes that the logistic regression model fitted with LASSO outperforms AdaBoost.M1 and decision tree in terms of both AUC score as well as predictive accuracy. Accurate prediction of 30-day mortality based on this research can be useful in risk stratification, individualized treatment, and patient management.Item Alternation by Gerbils and Rats during Practice(Animal Behavior Society Annual Meeting, 2004-05) Mercado III, E.; Ruch, M. C.; Liu, H. M.; Shan, Xiaojun; Sidaras, S. K.; Bogdan, M. L.Abstract not available.Item Applying Lean Six Sigma for Performance Improvement in Academic Advising(Proceedings of the 4th Annual World Conference of the Society for Industrial and Systems Engineering, 2015-10-19) Rezaeiahari, M.; Alkhawaldeh, R.; Shan, Xiaojun; Khasawneh, M. T.; Srihari, K.Academic advising provides critical supports for students to have successful academic experience. On the other hand, academic advising is a complex and multifaceted process to satisfy students’ diverse needs. This research applies continuous performance improvement techniques to evaluate the effectiveness of an academic advising office at the undergraduate level at a U.S. university with an annual average enrollment of approximately 8,000 students. The objective of this study is fourfold: (1) to analyze the advising office, (2) to identify operational bottlenecks, (3) to provide recommendations and streamline advising processes, and (4) to develop a comprehensive process improvement strategy. Recommendations are provided based on a DMAIC-based framework. To limit student waiting time to 20 or 10 minutes, 13 or 16 advisers are required during regular time periods, and 16 or 18 advisers are needed during the pre-registration time period, respectively, given a threefold increase in demand.Item Assessing the Policy Interaction Effect of Renewable Portfolio Standards (RPS) and Clean Power Plan (CPP) Emissions Goals for States in the U.S. Northeast(PowerEnergy2016, 2016-06-26) Chandramowli, S. N.; Felder, F. A.; Shan, XiaojunWith the proposed Clean Power Plan for regulating carbon emissions from the power sector in the U.S, policymakers are likely to use a cost optimization framework to plan for future scenarios and implementation strategies. The modeling framework introduced in this paper would help such policymakers to make the appropriate investment decisions for the power sector. This paper applies an analytical model and an optimization model to investigate the implications of coimplementing an emission cap and a Renewable Portfolio Standards (RPS) policy for the U.S. Northeast. A simplified analytical model is specified and the first order optimality conditions are derived. The results from the analytical model are verified by running simulations using LP-CEM, a linear programming-based supply cost optimization model. The LP-CEM simulation results are analyzed under the recently proposed Clean Power Plan emissions cap rules and RPS scenarios for the U.S. Northeast region. The marginal abatement cost estimates, derived from a limited set of LP-CEM runs, are analyzed and compared to the theoretical results. For encouraging renewables generation, an RPS instrument is costeffective at higher policy targets, while an emissions cap instrument is cost-effective at lower policy targets. For CO2 emissions reduction, an emissions cap instrument is found be cost-effective for all policy targets. There is a trade-off between emissions levels and supply costs when the two instruments are co-implemented.Item Benefits of Introspection and Self-Reflection in a Course on Effective Teaching Practices(Benefits of Introspection and Self-Reflection in a Course on Effective Teaching Practices, 2019-04-26) Kelling, A.; Lucas, A.; Watson, S.; Beavers, E.; Shan, Xiaojun; Bartsch, R.Abstract not available.Item Changes in NMDA Receptor Expression in Auditory Cortex after Learning(2005) Sun, W.; Mercado III, E.; Wang, P.; Shan, Xiaojun; Lee, T. C.; Salvi, R. J.Extensive practice on auditory learning tasks dramatically alters the functional organization and response properties of neurons in the auditory cortex. The cellular mechanisms responsible for this auditory learning-induced cortical plasticity are unclear; however, changes in synaptic function involving NMDA receptors have been strongly implicated. To test this hypothesis, we measured the change in gene expression of NMDA receptors and associated proteins in the auditory cortex of adult rats trained to perform an auditory identification task. NMDA receptor 2A and 2B gene expression in auditory cortex decreased significantly as auditory discrimination improved whereas expression of Arc, an immediate early gene involved in memory stabilization, increased. These results suggest that changes in NMDA receptors 2A and 2B and Arc enhance synaptic plasticity, thereby facilitating experience-dependent cortical remodeling and auditory learning.Item A Conceptual Framework for Developing a Staffing Model for Anticoagulation Clinics(2016 Healthcare Systems Process Improvement Conference, 2016-02-17) Hailemariam, D. A.; Shan, Xiaojun; Chung, S. H.; Khasawneh, M. T.Abstract not available.Item Cost of Equity in Defensive Resource Allocations in the Face of a Possibly Non-Strategic Attacker(State University of New York at Buffalo's Information and Computing Technology Workshop, 2011-04-06) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Defensive Resource Allocations in the Face of a Possibly Non-strategic Attacker(Society for Risk Analysis (SRA) Annual Meeting, 2011-12-04) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Defensive Resource Allocations in the Face of a Possibly Non-Strategic Attacker(The Institute for Operations Research and Management Science (INFORMS), 2012-10-14) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Defensive Resource Allocations in the Face of a Possibly Non-strategic Attacker(Decision Sciences Institute (DSI) Annual Meeting, 2011-11-19) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Defensive Resource Allocations in the Face of a Possibly Non-strategic Attacker(Decision Sciences Institute (DSI) Annual Meeting, 2011-11-19) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Defensive Resource Allocations in the Face of a Possibly Non-Strategic Attacker(State University of New York at Buffalo Sigma Xi Research Day and Poster Competition, 2011-04-06) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Defensive Resource Allocations in the Face of a Possibly Non-Strategic Attacker(INFORMS Annual Meeting, 2011-11-13) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Defensive Resource Allocations in the Face of a Possibly Non-Strategic Attacker(Second New York Conference on Applied Mathematics, 2011-04-30) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Defensive Resource Allocations in the Face of a Strategic Attacker(Institute for Operations Research and Management Science (INFORMS) Annual Meeting, 2012-10-14) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Defensive Resource Allocatoins in the Face of a Possibly Non-Strategic Attacker(2012 Industrial and Systems Engineering Research Conference (ISERC), 2012-05-18) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Homeland Security Resource Allocations in the Face of a Possibly Non-strategic Attacker(Institute for Operations Research and Management Science (INFORMS) Computing Society Conference, 2011-01-09) Shan, Xiaojun; Zhuang, J.Abstract not available.Item Cost of Equity in Homeland Security Resource Allocations in the Face of a Possibly Non-Strategic Attacker(Society for Risk Analysis (SRA) Annual Meeting, 2010-12-05) Shan, Xiaojun; Zhuang, J.Abstract not available.