The Experts below are selected from a list of 40938 Experts worldwide ranked by ideXlab platform
Martin S Zand - One of the best experts on this subject based on the ideXlab platform.
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predicting hospital onset clostridium difficile using patient mobility data a Network approach
Infection Control and Hospital Epidemiology, 2019Co-Authors: Kristen Bush, Hugo Barbosa, Samir A Farooq, Samuel J Weisenthal, Melissa Trayhan, Robert J White, Ekaterina I Noyes, Gourab Ghoshal, Martin S ZandAbstract:OBJECTIVE To examine the relationship between unit-wide Clostridium difficile infection (CDI) susceptibility and inpatient mobility and to create contagion Centrality as a new predictive measure of CDI. DESIGN Retrospective cohort study. METHODS A mobility Network was constructed using 2 years of patient electronic health record data for a 739-bed hospital (n = 72,636 admissions). Network Centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (ie, edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and were compared to Network Centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. RESULTS Closeness Centrality was a statistically significant measure associated with unit susceptibility (P < .05), highlighting the importance of incoming patient mobility in CDI prevention at the unit level. Contagion Centrality (CC) was calculated using inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. The contagion Centrality measure was statistically significant (P < .05) with our outcome of hospital-onset CDI cases, and it captured the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create easily interpretable clinical tools showing this relationship as well as the risk of hospital-onset CDI in real time, and these tools can be implemented in hospital EHR systems. CONCLUSIONS Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak and, thus, provide clinicians and infection prevention staff with advanced warning and specific location data to inform prevention efforts.
Kristen Bush - One of the best experts on this subject based on the ideXlab platform.
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predicting hospital onset clostridium difficile using patient mobility data a Network approach
Infection Control and Hospital Epidemiology, 2019Co-Authors: Kristen Bush, Hugo Barbosa, Samir A Farooq, Samuel J Weisenthal, Melissa Trayhan, Robert J White, Ekaterina I Noyes, Gourab Ghoshal, Martin S ZandAbstract:OBJECTIVE To examine the relationship between unit-wide Clostridium difficile infection (CDI) susceptibility and inpatient mobility and to create contagion Centrality as a new predictive measure of CDI. DESIGN Retrospective cohort study. METHODS A mobility Network was constructed using 2 years of patient electronic health record data for a 739-bed hospital (n = 72,636 admissions). Network Centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (ie, edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and were compared to Network Centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. RESULTS Closeness Centrality was a statistically significant measure associated with unit susceptibility (P < .05), highlighting the importance of incoming patient mobility in CDI prevention at the unit level. Contagion Centrality (CC) was calculated using inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. The contagion Centrality measure was statistically significant (P < .05) with our outcome of hospital-onset CDI cases, and it captured the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create easily interpretable clinical tools showing this relationship as well as the risk of hospital-onset CDI in real time, and these tools can be implemented in hospital EHR systems. CONCLUSIONS Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak and, thus, provide clinicians and infection prevention staff with advanced warning and specific location data to inform prevention efforts.
Ding Shuxin - One of the best experts on this subject based on the ideXlab platform.
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NC-MOPSO: Network Centrality guided multi-objective particle swarm optimization for transport optimization on Networks
2021Co-Authors: Wu Jiexin, Pu Cunlai, Ding Shuxin, Cao Guo, Pardalos, Panos M.Abstract:Transport processes are universal in real-world complex Networks, such as communication and transportation Networks. As the increase of the traffic in these complex Networks, problems like traffic congestion and transport delay are becoming more and more serious, which call for a systematic optimization of these Networks. In this paper, we formulate a multi-objective optimization problem (MOP) to deal with the enhancement of Network capacity and efficiency simultaneously, by appropriately adjusting the weights of edges in Networks. To solve this problem, we provide a multi-objective evolutionary algorithm (MOEA) based on particle swarm optimization (PSO), namely Network Centrality guided multi-objective PSO (NC-MOPSO). Specifically, in the framework of PSO, we propose a hybrid population initialization mechanism and a local search strategy by employing the Network Centrality theory to enhance the quality of initial solutions and strengthen the exploration of the search space, respectively. Simulation experiments performed on Network models and real Networks show that our algorithm has better performance than four state-of-the-art alternatives on several most-used metrics.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl
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NC-MOPSO: A Network Centrality guided multi-objective particle swarm optimization for transport optimization on Networks
2020Co-Authors: Wu Jiexin, Pu Cunlai, Ding ShuxinAbstract:Transport processes are universal in real-world complex Networks, such as communication and transportation Networks. As the increase of the traffic in these complex Networks, problems like traffic congestion and transport delay are becoming more and more serious, which call for a systematic optimization of these Networks. In this paper, we formulate a multi-objective optimization problem (MOP) to deal with the enhancement of Network capacity and efficiency simultaneously, by appropriately adjusting the weights of edges in Networks. To solve this problem, we provide a multi-objective evolutionary algorithm (MOEA) based on particle swarm optimization (PSO) with crowding distance, namely Network Centrality guided multi-objective PSO (NC-MOPSO). Specifically, in the framework of PSO, we propose a hybrid population initialization mechanism and a local search strategy by employing the Network Centrality theory to enhance the quality of initial solutions and strengthen the exploration of the search space, respectively. Simulation experiments performed on Network models and real Networks show that our algorithm has better performance than three state-of-the-art alternatives on several most-used metrics.Comment: 12 pages, 7 figure
Samuel J Weisenthal - One of the best experts on this subject based on the ideXlab platform.
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predicting hospital onset clostridium difficile using patient mobility data a Network approach
Infection Control and Hospital Epidemiology, 2019Co-Authors: Kristen Bush, Hugo Barbosa, Samir A Farooq, Samuel J Weisenthal, Melissa Trayhan, Robert J White, Ekaterina I Noyes, Gourab Ghoshal, Martin S ZandAbstract:OBJECTIVE To examine the relationship between unit-wide Clostridium difficile infection (CDI) susceptibility and inpatient mobility and to create contagion Centrality as a new predictive measure of CDI. DESIGN Retrospective cohort study. METHODS A mobility Network was constructed using 2 years of patient electronic health record data for a 739-bed hospital (n = 72,636 admissions). Network Centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (ie, edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and were compared to Network Centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. RESULTS Closeness Centrality was a statistically significant measure associated with unit susceptibility (P < .05), highlighting the importance of incoming patient mobility in CDI prevention at the unit level. Contagion Centrality (CC) was calculated using inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. The contagion Centrality measure was statistically significant (P < .05) with our outcome of hospital-onset CDI cases, and it captured the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create easily interpretable clinical tools showing this relationship as well as the risk of hospital-onset CDI in real time, and these tools can be implemented in hospital EHR systems. CONCLUSIONS Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak and, thus, provide clinicians and infection prevention staff with advanced warning and specific location data to inform prevention efforts.
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Predicting hospital-onset Clostridium difficile using patient mobility data: A Network approach
'Cambridge University Press (CUP)', 2019Co-Authors: Bush K, Samuel J Weisenthal, Barbosa H, Farooq S, Trayhan M, Rj White, Ei Noyes, Ghoshal G, Zand MsAbstract:This is the final version. Available from Cambridge University Press via the DOI in this record. Objective: To examine the relationship between unit-wide Clostridium difficile infection (CDI) susceptibility and inpatient mobility and to create contagion Centrality as a new predictive measure of CDI. Design: Retrospective cohort study. Methods: A mobility Network was constructed using 2 years of patient electronic health record data for a 739-bed hospital (n = 72,636 admissions). Network Centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (ie, edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and were compared to Network Centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. Results: Closeness Centrality was a statistically significant measure associated with unit susceptibility (P< .05), highlighting the importance of incoming patient mobility in CDI prevention at the unit level. Contagion Centrality (CC) was calculated using inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. The contagion Centrality measure was statistically significant (P< .05) with our outcome of hospital-onset CDI cases, and it captured the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create easily interpretable clinical tools showing this relationship as well as the risk of hospital-onset CDI in real time, and these tools can be implemented in hospital EHR systems Conclusions: Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak and, thus, provide clinicians and infection prevention staff with advanced warning and specific location data to inform prevention efforts.University of Rochester Clinical and Translational Science InstituteNational Institutes of HealthBurroughs Wellcome Fund Institutional Program Unifying Population and Laboratory Based Science
Jan Alexander Häusser - One of the best experts on this subject based on the ideXlab platform.
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Interactive effects of social Network Centrality and social identification on stress
British Journal of Psychology, 2020Co-Authors: Andreas Mojzisch, Johanna U. Frisch, Malte Doehne, Maren Reder, Jan Alexander HäusserAbstract:The present study aimed to integrate the social identity approach to health and well-being with social Network analysis. Previous research on the effects of social Network Centrality on stress has yielded mixed results. Building on the social identity approach, we argued that these mixed results can be explained, in part, by taking into account the degree to which individuals identify with the social Network. We hence hypothesized that the effects of social Network Centrality on stress are moderated by social identification. Using a full roster method, we assessed the social Network of first-year psychology students right after the start of their study programme and three months later. The effects of Network Centrality (betweenness, closeness, eigenvector Centrality) and social identification on stress were examined using structural equation models. As predicted, our results revealed a significant interaction between Network Centrality and social identification on stress: For weakly or moderately identified students, Network Centrality was positively related to stress. By contrast, for strongly identified students, Network Centrality was unrelated to stress. In conclusion, our results point to the perils of being well-connected yet not feeling like one belongs to a group.
