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      A systematic approach to analyze the social determinants of cardiovascular disease

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          Abstract

          Cardiovascular diseases are the leading cause of human mortality worldwide. Among the many factors associated with the etiology, incidence, and evolution of such diseases; social and environmental issues constitute an important and often overlooked component. Understanding to a greater extent the scope to which such social determinants of cardiovascular diseases (SDCVD) occur as well as the connections among them would be useful for public health policy making. Here, we will explore the historical trends and associations among the main SDCVD in the published literature. Our aim will be finding meaningful relations among those that will help us to have an integrated view on this complex phenomenon by providing historical context and a relational framework. To uncover such relations, we used a data mining approach to the current literature, followed by network analysis of the interrelationships discovered. To this end, we systematically mined the PubMed/MEDLINE database for references of published studies on the subject, as outlined by the World Health Organization’s framework on social determinants of health. The analyzed structured corpus consisted in circa 1190 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor. The use of data analytics techniques allowed us to find a number of non-trivial connections among SDCVDs. Such relations may be relevant to get a deeper understanding of the social and environmental issues associated with cardiovascular disease and are often overlooked by traditional literature survey approaches, such as systematic reviews and meta-analyses.

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          Most cited references26

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          The World Health Organization MONICA Project (monitoring trends and determinants in cardiovascular disease): a major international collaboration. WHO MONICA Project Principal Investigators.

          A World Health Organization Working Group has developed a major international collaborative study with the objective of measuring over 10 years, and in many different populations, the trends in, and determinants of, cardiovascular disease. Specifically the programme focuses on trends in event rates for validated fatal and non-fatal coronary heart attacks and strokes, and on trends in cardiovascular risk factors (blood pressure, cigarette smoking and serum cholesterol) in men and women aged 25-64 in the same defined communities. By this means it is hoped both to measure changes in cardiovascular mortality and to see how far they are explained; on the one hand by changes in incidence mediated by risk factor levels; and on the other by changes in case-fatality rates, related to medical care. Population centres need to be large and numerous; to reliably establish 10-year trends in event rates within a centre 200 or more fatal events in men per year are needed, while for the collaborative study a multiplicity of internally homogeneous centres showing differing trends will provide the best test of the hypotheses. Forty-one MONICA Collaborating Centres, using a standardized protocol, are studying 118 Reporting Units (subpopulations) with a total population aged 25-64 (both sexes) of about 15 million.
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            Topological analysis and interactive visualization of biological networks and protein structures.

            Computational analysis and interactive visualization of biological networks and protein structures are common tasks for gaining insight into biological processes. This protocol describes three workflows based on the NetworkAnalyzer and RINalyzer plug-ins for Cytoscape, a popular software platform for networks. NetworkAnalyzer has become a standard Cytoscape tool for comprehensive network topology analysis. In addition, RINalyzer provides methods for exploring residue interaction networks derived from protein structures. The first workflow uses NetworkAnalyzer to perform a topological analysis of biological networks. The second workflow applies RINalyzer to study protein structure and function and to compute network centrality measures. The third workflow combines NetworkAnalyzer and RINalyzer to compare residue networks. The full protocol can be completed in ∼2 h.
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              Data mining in healthcare and biomedicine: a survey of the literature.

              As a new concept that emerged in the middle of 1990's, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets. Data mining can uncover new biomedical and healthcare knowledge for clinical and administrative decision making as well as generate scientific hypotheses from large experimental data, clinical databases, and/or biomedical literature. This review first introduces data mining in general (e.g., the background, definition, and process of data mining), discusses the major differences between statistics and data mining and then speaks to the uniqueness of data mining in the biomedical and healthcare fields. A brief summarization of various data mining algorithms used for classification, clustering, and association as well as their respective advantages and drawbacks is also presented. Suggested guidelines on how to use data mining algorithms in each area of classification, clustering, and association are offered along with three examples of how data mining has been used in the healthcare industry. Given the successful application of data mining by health related organizations that has helped to predict health insurance fraud and under-diagnosed patients, and identify and classify at-risk people in terms of health with the goal of reducing healthcare cost, we introduce how data mining technologies (in each area of classification, clustering, and association) have been used for a multitude of purposes, including research in the biomedical and healthcare fields. A discussion of the technologies available to enable the prediction of healthcare costs (including length of hospital stay), disease diagnosis and prognosis, and the discovery of hidden biomedical and healthcare patterns from related databases is offered along with a discussion of the use of data mining to discover such relationships as those between health conditions and a disease, relationships among diseases, and relationships among drugs. The article concludes with a discussion of the problems that hamper the clinical use of data mining by health professionals.
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                Author and article information

                Contributors
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2018
                25 January 2018
                : 13
                : 1
                : e0190960
                Affiliations
                [1 ] Sociomedical Research Department, National Institute of Cardiology, Mexico City, Mexico
                [2 ] Metropolitan Autonomous University (UAM), Xochimilco, Mexico City, Mexico
                [3 ] Health Science School, University of the Valley of Mexico (UVM), Mexico City, Mexico
                [4 ] Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
                [5 ] Center for Complexity Sciences, National Autonomous University of Mexico, Mexico City, Mexico
                The Chinese University of Hong Kong, HONG KONG
                Author notes

                Competing Interests: EHL is an Academic Editor at PLOS ONE. The authors have declared that no other competing interests exist. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                [¤]

                Current address: National Laboratory for Sustainability, Institute of Ecology, National Autonomous University of Mexico, Mexico City, Mexico

                Author information
                http://orcid.org/0000-0001-5074-2473
                Article
                PONE-D-17-18726
                10.1371/journal.pone.0190960
                5784921
                29370200
                16468e32-590e-4671-a374-35ae9a088beb
                © 2018 Martínez-García et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 16 May 2017
                : 23 December 2017
                Page count
                Figures: 11, Tables: 1, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100003141, Consejo Nacional de Ciencia y Tecnología;
                Award ID: 179431
                Award Recipient :
                This work was supported by CONACYT (grant no.179431/2012) [EHL], as well as by federal funding from the National Institute of Cardiology (Mexico) [MV] and the National Institute of Genomic Medicine (Mexico) [EHL]. Mireya Martínez-García is a doctoral candidate in the Ph.D. Programme in Collective Health supported by a CONACYT Fellowship. [EHL] acknowledges additional support from the 2016 Marcos Moshinsky Chair in the Physical Sciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Public and Occupational Health
                Behavioral and Social Aspects of Health
                Medicine and Health Sciences
                Health Care
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Public and Occupational Health
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Public and Occupational Health
                Global Health
                Medicine and Health Sciences
                Health Care
                Health Care Policy
                Medicine and Health Sciences
                Cardiovascular Medicine
                Cardiovascular Diseases
                Computer and Information Sciences
                Network Analysis
                Research and Analysis Methods
                Database and Informatics Methods
                Database Searching
                Social Sciences
                Sociology
                Social Policy
                Custom metadata
                All relevant data are within the paper and its Supporting Information files. Relevant programming code can be found in the following electronic address: https://github.com/CSB-IG/bibliometrics.

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                Uncategorized

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