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Eric Xu
Researcher at University of Minnesota
Publications - 9
Citations - 2726
Eric Xu is an academic researcher from University of Minnesota. The author has contributed to research in topics: Information system & Enterprise information system. The author has an hindex of 6, co-authored 9 publications receiving 1974 citations. Previous affiliations of Eric Xu include Florida State University College of Arts and Sciences & Columbia University.
Papers
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Industry 4.0: state of the art and future trends
TL;DR: The state of the art in the area of Industry 4.0 as it relates to industries is surveyed, with a focus on China's Made-in-China 2025 and formal methods and systems methods crucial for realising Industry 5.0.
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Wild-type microglia arrest pathology in a mouse model of Rett syndrome
Noël C. Derecki,James C. Cronk,Zhenjie Lu,Eric Xu,Stephen B. G. Abbott,Patrice G. Guyenet,Jonathan Kipnis +6 more
TL;DR: The data implicate microglia as major players in the pathophysiology of this devastating disorder, and suggest that bone marrow transplantation might offer a feasible therapeutic approach for it.
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Healthcare information systems: data mining methods in the creation of a clinical recommender system
Lian Duan,W.N. Street,Eric Xu +2 more
TL;DR: The proposed system uses correlations among nursing diagnoses, outcomes and interventions to create a recommender system for constructing nursing care plans, and utilises a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items.
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Development of an integrated medical supply information system
Eric Xu,Marek Wermus,Bauman Db +2 more
TL;DR: The pilot case study demonstrates that the integrated medical supply information system holds several advantages for inventory managers, since it entails benefits of deploying enterprise information systems to manage medical supply and better patient services.
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On an ensemble algorithm for clustering cancer patient data
Ran Qi,Dengyuan Wu,Li Sheng,Donald E. Henson,Arnold M. Schwartz,Eric Xu,Kai Xing,Dechang Chen +7 more
TL;DR: Examining various aspects of EACCD using a large breast cancer patient dataset concluded that when only the Partitioning Around Medoids (PAM) algorithm is involved in the step of learning dissimilarity, large values of m are required to obtain robust dendrograms, and for a large m EAC CD can effectively cluster cancer patient data.