scholarly journals Symptom Correlates Using Network Analysis in Pediatric Patients Undergoing Blood and Marrow Transplant

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4978-4978
Author(s):  
Shannon Ford ◽  
Jacqueline Vaughn ◽  
Arvind Subramaniam ◽  
Abhinav Gundala ◽  
Nirmish Shah
Keyword(s):  
Network Analysis ◽  
Pain Intensity ◽  
Sore Throat ◽  
Research Funding ◽  
Average Distance ◽  
Negative Relationship ◽  
Strong Relationship ◽  
Centrality Measures ◽  
Symptom Experience ◽  
Partial Correlations

Abstract Introduction: Youth undergoing blood and marrow transplantation (BMT) experience significant distress. The widespread and mainstream use of mHealth technologies such as smartphone applications offer a unique opportunity to collect patient-generated health data that can enhance patients' and clinicians' understanding of symptom onset, trajectories, and inter-relationships. Network analysis (NA) is a useful tool that can illuminate inter-relationships between symptoms, leading to better collective understanding of the symptom experiences of these youths. This knowledge can lead to enhanced patient-caregiver interactions/collaborations, treatment management, and potentially support improved inference around adverse clinical outcomes. Objective: To determine the feasibility of using network analysis to evaluate mHealth symptom data in patients undergoing BMT. Aim 1. Estimate the network by creating a graphic model that depicts symptom inter-relationships. Aim 2. Identify influential symptoms in the network by evaluating centrality indices and assessing the stability of edges and centrality results. Methods : Study participants undergoing BMT recorded daily symptoms via a smartphone app from preconditioning through 120 days. Patients report their symptom experience (intensity and distress on a scale of 0-10). NA was conducted on the initial patients (n=3) to evaluate inter-relationships between reported symptoms. Each symptom is represented by a circle (node), while associations (regularized partial correlations coefficients) between symptoms are depicted as lines (edges). Stronger associations among symptoms present as thicker lines in the network and a higher value in the weighted matrix table. Centrality indices identify and quantify symptoms that exert more influence in the network. These symptoms are influential in the network due to their strength (sum of absolute values of its connections with other nodes), betweenness (number of times a node lies on the shortest path between two other nodes), or closeness (the summed average distance of a node to all other nodes). Other centrality measures also exist. Centrality tests aimed to evaluate symptoms for their reported importance to the youth and association of those symptoms with other reported symptoms. The more "central" a symptom, the higher the potential to transmit effects to and from other symptoms in the network. This can make them important foci for intervention. Stability testing was used to assess the network's accuracy. Results/Discussion: Descriptive statistics are summarized in Table 1. The estimated network (Fig. 1) provided details on the eight most often reported symptoms; nausea, tired (intensity and distress), vomiting, pain, mouth pain, and sore throat (intensity). The network shows strong mutual associations (regularized partial correlations) between the intensity and distress of nausea (.596) and being tired (.722). There was also a strong relationship between mouth pain and sore throat (.791). A less strong relationship was noted between nausea intensity and tired intensity (.145), and tired intensity and pain intensity (.187). A slight negative relationship was noted between vomiting intensity and pain intensity (-.086). The centrality indices (Fig. 2) revealed vomiting intensity (-1.917) as the strongest symptom with the highest closeness (-1.715). The symptom with the highest betweenness centrality was equal for both nausea intensity and tired intensity at 1.286. To assess confidence in the network estimation we replicated this test 1000 times (non-parametric bootstrapping, n=1000) on the edge stability and centrality results (Fig. 3). Results indicate areas with wide confidence intervals (instability) especially in the edge between vomiting intensity and mouth pain. Conclusion: It is feasible to use mHealth data from youth who experience symptom distress during BMT. However, efforts to obtain more data is necessary if we hope to make accurate inferences from the data. Future work will focus on enriching data collection, examining clinically important sign and symptom patterns and interrelationships, and to explore feasibility of using mHealth data for individualized/precision care and possible predictive uses. Figure 1 Figure 1. Disclosures Shah: Novartis: Research Funding, Speakers Bureau; GBT: Consultancy, Research Funding, Speakers Bureau; CSL Behring: Consultancy; Guidepoint Global: Consultancy; Alexion: Speakers Bureau; Bluebird Bio: Consultancy; Emmaus: Consultancy; GLG: Consultancy.

scholarly journals Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality

Psychometrika ◽  
2021 ◽  
Author(s):  
Oisín Ryan ◽  
Ellen L. Hamaker
Keyword(s):  
Network Analysis ◽  
Discrete Time ◽  
Continuous Time ◽  
Centrality Measures ◽  
Time Interval ◽  
Direct Effects ◽  
Network Approach ◽  
Var Models ◽  
Auto Regressive ◽  
Conceptual Shift

AbstractNetwork analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

scholarly journals Visualization of clinical teaching citations using social network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hakimeh Hazrati ◽  
Shoaleh Bigdeli ◽  
Seyed Kamran Soltani Arabshahi ◽  
Vahideh Zarea Gavgani ◽  
Nafiseh Vahed
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Collaboration ◽  
Clinical Teaching ◽  
Clustering Coefficient ◽  
Knowledge Network ◽  
Research Network ◽  
Centrality Measures ◽  
The Usa

Abstract Background Analyzing the previous research literature in the field of clinical teaching has potential to show the trend and future direction of this field. This study aimed to visualize the co-authorship networks and scientific map of research outputs of clinical teaching and medical education by Social Network Analysis (SNA). Methods We Identified 1229 publications on clinical teaching through a systematic search strategy in the Scopus (Elsevier), Web of Science (Clarivate Analytics) and Medline (NCBI/NLM) through PubMed from the year 1980 to 2018.The Ravar PreMap, Netdraw, UCINet and VOSviewer software were used for data visualization and analysis. Results Based on the findings of study the network of clinical teaching was weak in term of cohesion and the density in the co-authorship networks of authors (clustering coefficient (CC): 0.749, density: 0.0238) and collaboration of countries (CC: 0.655, density: 0.176). In regard to centrality measures; the most influential authors in the co-authorship network was Rosenbaum ME, from the USA (0.048). More, the USA, the UK, Canada, Australia and the Netherlands have central role in collaboration countries network and has the vertex co-authorship with other that participated in publishing articles in clinical teaching. Analysis of background and affiliation of authors showed that co-authorship between clinical researchers in medicine filed is weak. Nineteen subject clusters were identified in the clinical teaching research network, seven of which were related to the expected competencies of clinical teaching and three related to clinical teaching skills. Conclusions In order to improve the cohesion of the authorship network of clinical teaching, it is essential to improve research collaboration and co-authorship between new researchers and those who have better closeness or geodisk path with others, especially those with the clinical background. To reach to a dense and powerful topology in the knowledge network of this field encouraging policies to be made for international and national collaboration between clinicians and clinical teaching specialists. In addition, humanitarian and clinical reasoning need to be considered in clinical teaching as of new direction in the field from thematic aspects.

scholarly journals A New Methodology to Study Street Accessibility: A Case Study of Avila (Spain)

2021 ◽  
Vol 10 (7) ◽  
pp. 491
Author(s):  
Manuel Curado ◽  
Rocio Rodriguez ◽  
Manuel Jimenez ◽  
Leandro Tortosa ◽  
Jose F. Vicent
Keyword(s):  
Network Analysis ◽  
The Other ◽  
Centrality Measures ◽  
Innovative Behavior ◽  
Street Network ◽  
Commercial Activity ◽  
Determining Factors ◽  
Combine Network ◽  
The City

Taking into account that accessibility is one of the most strategic and determining factors in economic models and that accessibility and tourism affect each other, we can say that the study and improvement of one of them involved the development of the other. Using network analysis, this study presents an algorithm for labeling the difficulty of the streets of a city using different accessibility parameters. We combine network structure and accessibility factors to explore the association between innovative behavior within the street network, and the relationships with the commercial activity in a city. Finally, we present a case study of the city of Avila, locating the most inaccessible areas of the city using centrality measures and analyzing the effects, in terms of accessibility, on the commerce and services of the city.

scholarly journals A case study of university student networks and the COVID-19 pandemic using a social network analysis approach in halls of residence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Alberto Benítez-Andrades ◽  
Tania Fernández-Villa ◽  
Carmen Benavides ◽  
Andrea Gayubo-Serrenes ◽  
Vicente Martín ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
University Students ◽  
Methodological Approach ◽  
Centrality Measures ◽  
Structural Behaviour ◽  
Cross Sectional ◽  
Depth Analysis ◽  
The University

AbstractThe COVID-19 pandemic has meant that young university students have had to adapt their learning and have a reduced relational context. Adversity contexts build models of human behaviour based on relationships. However, there is a lack of studies that analyse the behaviour of university students based on their social structure in the context of a pandemic. This information could be useful in making decisions on how to plan collective responses to adversities. The Social Network Analysis (SNA) method has been chosen to address this structural perspective. The aim of our research is to describe the structural behaviour of students in university residences during the COVID-19 pandemic with a more in-depth analysis of student leaders. A descriptive cross-sectional study was carried out at one Spanish Public University, León, from 23th October 2020 to 20th November 2020. The participation was of 93 students, from four halls of residence. The data were collected from a database created specifically at the university to "track" contacts in the COVID-19 pandemic, SiVeUle. We applied the SNA for the analysis of the data. The leadership on the university residence was measured using centrality measures. The top leaders were analyzed using the Egonetwork and an assessment of the key players. Students with higher social reputations experience higher levels of pandemic contagion in relation to COVID-19 infection. The results were statistically significant between the centrality in the network and the results of the COVID-19 infection. The most leading students showed a high degree of Betweenness, and three students had the key player structure in the network. Networking behaviour of university students in halls of residence could be related to contagion in the COVID-19 pandemic. This could be described on the basis of aspects of similarities between students, and even leaders connecting the cohabitation sub-networks. In this context, Social Network Analysis could be considered as a methodological approach for future network studies in health emergency contexts.

Social network analysis applications in sustainable construction and built environment management: a review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nandun Madhusanka Hewa Welege ◽  
Wei Pan ◽  
Mohan Kumaraswamy
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Built Environment ◽  
Methodological Approach ◽  
Relevant Literature ◽  
Sustainable Construction ◽  
Centrality Measures ◽  
Environment Management ◽  
Content Type

PurposeApplications of social network analysis (SNA) are evidently popular amongst scholars for mapping stakeholder and other relational networks in improving the sustainability of construction activities and the resulting built environment. Nevertheless, the literature reveals a lack of thorough understanding of optimal SNA applications in this field. Therefore, this paper aims to convey a comprehensive critical review of past applications of SNA in this field.Design/methodology/approach95 relevant journal papers were initially identified from the “Web of Science” database and a bibliometric analysis was carried out using the “VOS Viewer” software. The subsequent in-depth review of the SNA methods, focussed on 24 specifically relevant papers selected from these aforesaid 95 papers.FindingsA significant growth of publications in this field was identified after 2014, especially related to topics on stakeholder management. “Journal of Cleaner Production”, “International Journal of Project Management” and “Sustainability” were identified as the most productive sources in this field, with the majority of publications from China. Interviews and questionnaires were the popular data collection methods while SNA “Centrality” measures were utilised in over 70% of the studies. Furthermore, potential areas were noted, to improve the mapping and thereby provide useful information to managers who could influence relevant networks and consequentially better sustainability outcomes, including those enhanced by collaborative networks.Originality/valueCloser collaboration has been found to help enhance sustainability in construction and built environment, hence attracting research interest amongst scholars on how best to enable this. SNA is established as a significant methodological approach to analysing interrelationships and collaborative potential in general. In a pioneering application here, this paper initiates the drawing together of findings from relevant literature to provide useful insights for future researchers to comprehensively identify, compare and contrast the applications of SNA techniques in construction and built environment management from a sustainability viewpoint.

scholarly journals Length of productive life the cows of Simmental breed

2021 ◽  
Vol 213 (10) ◽  
pp. 31-39
Author(s):  
L. Ignat'eva ◽  
A. Sermyagin
Keyword(s):  
Milk Yield ◽  
Russian Federation ◽  
Negative Relationship ◽  
Strong Relationship ◽  
Cattle Breeding ◽  
Productive Life ◽  
The Russian Federation ◽  
The Republic ◽  
First Time ◽  
The Relationship

Abstract. The purpose of the research was to assess the duration of the length of productive life of Simmental cows. Methods. The research was carried out on Simmental cows bred in 14 regions of the Russian Federation, the total livestock was 8 832 heads. The calculation of the heritability coefficients and correlation (genetic and paratypic) was carried out by using the programs RENUMF90 and REMLF90. Results. A fairly strong relationship was established between the duration of a productive life (months) and the age of culling (lactations) r = +0.795, the length of productive life (months) and lifetime productivity within the range of +0.669…+0.714. However, the relationship between the age of culling (lactations) and lifetime productivity is moderate, from +0.261 to +0.316. A moderate negative relationship was obtained between the age of culling (lactations) and milk yield per first lactation from –0.472 to –0.486. The average relationship was found between milk yield per first lactation and lifetime productivity from +0.567 to +0.588. Cows of the Altai Territory (3.08 lactations or 61.6 months), the Republic of Mordovia (3.38 lactations or 62.4 months) and the Lipetsk region (3.40 lactations or 65.7 months) were distinguished by low age of culling. While the greatest length of productive life was noted in animals and Bryansk (5.48 lactations or 86.9 months) and Irkutsk regions (4.57 lactations or 77.1 months). Bryansk (23 630 kg of milk), Tyumen (18 156 kg) and Irkutsk (17 751 kg) regions occupied the leading positions in lifetime productivity of cows in the sample, while the outsiders were the regions of traditional cattle breeding - Altai Territory (12658 kg of milk), the Republic of Bashkiria (12 482 kg). Scientific novelty. For the population Simmental cattle of the Russian Federation, for the first time, an assessment of selection and genetic parameters of lifelong productivity and length of productive life of Simmental cows was carried out, depending on the breeding region.

scholarly journals Bayesian Uncertainty Estimation for Gaussian Graphical Models and Centrality Indices

2021 ◽  
Author(s):  
Joran Jongerling ◽  
Sacha Epskamp ◽  
Donald Ray Williams
Keyword(s):  
Bayesian Estimation ◽  
Graphical Models ◽  
Estimation Methods ◽  
Centrality Measures ◽  
Gaussian Graphical Models ◽  
Graphical Lasso ◽  
Study Results ◽  
Partial Correlations ◽  
Good Coverage ◽  
Better Than

Gaussian Graphical Models (GGMs) are often estimated using regularized estimation and the graphical LASSO (GLASSO). However, the GLASSO has difficulty estimating(uncertainty in) centrality indices of nodes. Regularized Bayesian estimation might provide a solution, as it is better suited to deal with bias in the sampling distribution ofcentrality indices. This study therefore compares estimation of GGMs with a Bayesian GLASSO- and a Horseshoe prior to estimation using the frequentist GLASSO in an extensive simulation study. Results showed that out of the two Bayesian estimation methods, the Bayesian GLASSO performed best. In addition, the Bayesian GLASSOperformed better than the frequentist GLASSO with respect to bias in edge weights, centrality measures, correlation between estimated and true partial correlations, andspecificity. With respect to sensitivity the frequentist GLASSO performs better.However, sensitivity of the Bayesian GLASSO is close to that of the frequentist GLASSO (except for the smallest N used in the simulations) and tends to be favored over the frequentist GLASSO in terms of F1. With respect to uncertainty in the centrality measures, the Bayesian GLASSO shows good coverage for strength andcloseness centrality. Uncertainty in betweenness centrality is estimated less well, and typically overestimated by the Bayesian GLASSO.

scholarly journals A Matrix-Variate t Model for Networks

2021 ◽  
Vol 4 ◽  
Author(s):  
Monica Billio ◽  
Roberto Casarin ◽  
Michele Costola ◽  
Matteo Iacopini
Keyword(s):  
Network Analysis ◽  
Measurement Errors ◽  
Statistical Treatment ◽  
Centrality Measures ◽  
Random Errors ◽  
Inference Procedure ◽  
Financial Firms ◽  
The Matrix ◽  
Different Sources ◽  
Flexible Model

Networks represent a useful tool to describe relationships among financial firms and network analysis has been extensively used in recent years to study financial connectedness. An aspect, which is often neglected, is that network observations come with errors from different sources, such as estimation and measurement errors, thus a proper statistical treatment of the data is needed before network analysis can be performed. We show that node centrality measures can be heavily affected by random errors and propose a flexible model based on the matrix-variate t distribution and a Bayesian inference procedure to de-noise the data. We provide an application to a network among European financial institutions.

Exploring Network Analysis for Urban Planning and Disaster Risk Reduction in Informal Settlements

2020 ◽  
Vol 3 (1) ◽  
pp. 30-45
Author(s):  
Vicente Sandoval ◽  
Juan Pablo Sarmiento ◽  
Erick Alberto Mazariegos ◽  
Daniel Oviedo
Keyword(s):  
Network Analysis ◽  
Urban Planning ◽  
Risk Reduction ◽  
Disaster Risk Reduction ◽  
Informal Settlements ◽  
Disaster Risk ◽  
Centrality Measures ◽  
Analysis Tool ◽  
Urban Resilience ◽  
Street Networks

The work explores the use of street network analysis on informal settlements and discusses the potential and limitations of this methodology to advance disaster risk reduction and urban resilience. The urban network analysis tool is used to conduct graph analysis measures on street networks in three informal settlements in the LAC region: Portmore, Jamaica; Tegucigalpa, Honduras; and Lima, Peru. Authors incorporate risk variables identified by these communities and combine them with prospective scenarios in which street networks are strategically intervened to improve performance. Authors also compute one graph index named Reach centrality. Results are presented spatially through thematic maps, and statistically by plotting cumulative distributions. Findings show that centrality measures of settlements' networks helped identify key nodes or roads that may be critical for people's daily life after disasters, and strategic to improve accessibility. The proposed methodology shows potential to inform decisions on urban planning and disaster risk reduction.

scholarly journals Network Analysis of the Multidimensional Symptom Experience of Oncology

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Nikolaos Papachristou ◽  
Payam Barnaghi ◽  
Bruce Cooper ◽  
Kord M. Kober ◽  
Roma Maguire ◽  
...  
Keyword(s):  
Network Analysis ◽  
Symptom Experience
Sign in / Sign up

Export Citation Format

Share Document