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sample_data/lens.csv

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+Lens ID,Title,Date Published,Publication Year,Publication Type,Source Title,ISSNs,Publisher,Source Country,Author/s,Abstract,Volume,Issue Number,Start Page,End Page,Fields of Study,Keywords,MeSH Terms,Chemicals,Funding,Source URLs,External URL,PMID,DOI,Microsoft Academic ID,PMCID,Citing Patents Count,References,Citing Works Count,Is Open Access,Open Access License,Open Access Colour
+004-110-551-439-89X,OECD Health Data: Selected Data,2010-04-07,2010,dataset,OECD Health Statistics,,OECD Publishing,,,,,,,,Health data; Computer science; Political science; Health care; Law,,,,,,http://dx.doi.org/10.1787/data-00348-en,,10.1787/data-00348-en,,,0,,24,false,,
+005-518-089-099-041,Public Health Data,,2016,book chapter,Homicide Data Sources,21928533; 21928541,Springer International Publishing,,Adam Dobrin,"Relying on coroners’ reports instead of police submissions, the Center for Disease Control and Prevention’s National Center for Health Statistics National Vital Statistics System presents another large system to report homicides. This chapter will describe the system, its benefits, and offers some comparisons to police data on murders.",,,19,27,Public health informatics; Health education; Health administration; Public health; HRHIS; Political science; Health promotion; International health; Public relations; Health policy; Environmental health,,,,,https://link.springer.com/10.1007/978-3-319-19881-1_3 https://link.springer.com/chapter/10.1007/978-3-319-19881-1_3 https://rd.springer.com/chapter/10.1007/978-3-319-19881-1_3,http://dx.doi.org/10.1007/978-3-319-19881-1_3,,10.1007/978-3-319-19881-1_3,1410445404,,0,004-430-593-186-045; 005-901-007-640-001; 009-243-968-397-868; 065-859-327-369-599; 070-593-690-533-387; 074-984-960-046-874; 126-564-062-362-393; 135-977-096-037-400; 176-192-206-997-072,2,false,,
+006-832-463-649-675,MGH Guides: Health Data Sources: Visualizing Health Data,2018-11-01,2018,libguide,,,,,Amanda Tarbet,,,,,,Data science; Health data; Computer science,,,,,https://libguides.massgeneral.org/c.php?g=888627&p=6569090,https://libguides.massgeneral.org/c.php?g=888627&p=6569090,,,2994830172,,0,,0,false,,
+011-077-810-404-141,Research Guides: Health & Medical Data Sets : Global Health - Health & Medical Data Sets,2020-02-11,2020,libguide,,,,,Elizabeth Bucciarelli,,,,,,Data science; Computer science; Global health,,,,,https://guides.emich.edu/c.php?g=1002394&p=7263211,https://guides.emich.edu/c.php?g=1002394&p=7263211,,,3006035710,,0,,0,false,,
+012-245-468-954-434,Research Guides: Health Data and Statistics: Health Data and Statistics,2020-04-17,2020,libguide,,,,,Leslie Christensen,,,,,,Pharmacy; Psychology; Health data; Medicine public health; Medical education,,,,,https://researchguides.library.wisc.edu/health-statistics,https://researchguides.library.wisc.edu/health-statistics,,,3025010112,,0,,0,false,,
+013-652-112-211-482,Health data ecosystems: Sharing health data to facilitate medical progress,,2022,journal article,Regulatory Affairs Watch,28132548; 28132556,Swiss Clinical Trial Organisation,,Marc Engelhard,"<jats:p>Sharing health data in a meaningful way that preserves privacy is the foundation of a well-functioning digital health data ecosystem. A digital ecosystem implies that stakeholders are embedded in the necessary conditions to collect, store, share, and use health data electronically. Health data ecosystems can provide many benefits to society, including effective personalised medicine for patients, greater innovation in research, and improved policymaking. As an integral part of these health data ecosystems, the pharmaceutical industry already contributes substantially to them by investing in and sharing health data in order to facilitate medical progress. While many countries have recognised the value of health data ecosystems, Switzerland lags massively behind when it comes to secondary health data usage. To change this, Switzerland needs to develop a coherent strategy to create a health data ecosystem involving all relevant stakeholders.</jats:p>",4,7,22,24,Data sharing; Digital health; Ecosystem health; Business; Health data; Ecosystem; Order (exchange); Digital ecosystem; Environmental resource management; Data science; Computer science; Knowledge management; Ecosystem services; Health care; Medicine; Ecology; Political science; Economics; Alternative medicine; Finance; Pathology; Law; Biology,,,,,,http://dx.doi.org/10.54920/scto.2022.rawatch.7.22,,10.54920/scto.2022.rawatch.7.22,,,0,,0,true,,bronze
+019-295-317-119-300,Treatment of Health Data,2022-10-14,2022,book chapter,"Handbook of Research on Complexities, Management, and Governance in Healthcare",23281243; 2328126x,IGI Global,,Stefania De Angelis,"<jats:p>The pandemic, from the very beginning, has overwhelmed the everyday life of all people and has strongly crystallized the need to correctly manage personal data relating to the health of patients if they can be used for scientific research and the collection of statistical data. Patient consent, public interest, and public health run counter to the right to the protection of personal data of Covid-19 patients. A balance must be struck between fundamental rights such as the right to privacy, the protection of personal data, and the public interest which translates into the protection of public health.</jats:p>",,,115,126,Internet privacy; Public health; Data collection; Public interest; Everyday life; Pandemic; Right to privacy; Public life; Balance (ability); Health data; Coronavirus disease 2019 (COVID-19); Public relations; Business; Psychology; Political science; Medicine; Health care; Computer science; Sociology; Nursing; Politics; Disease; Law; Social science; Pathology; Neuroscience; Infectious disease (medical specialty),,,,,,http://dx.doi.org/10.4018/978-1-6684-6044-3.ch009,,10.4018/978-1-6684-6044-3.ch009,,,0,,0,false,,
+019-905-236-028-285,Health Data,2017-09-20,2017,book chapter,Essential Introduction to Understanding European Data Protection Rules,,Auerbach Publications,,Paul B. Lambert,,,,385,391,Computer science,,,,,,http://dx.doi.org/10.1201/9781315115269-36,,10.1201/9781315115269-36,,,0,,0,false,,
+020-249-686-321-588,Health Data Science,2021-06-01,2021,journal,,27658783,American Association for the Advancement of Science (AAAS),,,,,,,,Data science; Health science; Computer science; Medicine; Medical education,,,,,,http://dx.doi.org/10.34133/hds,,10.34133/hds,,,0,,0,true,cc-by,gold
+021-546-757-293-084,MGH Guides: Health Data Sources: Health Data Sources,2018-11-01,2018,libguide,,,,,Amanda Tarbet,,,,,,Geography; Health data; Environmental health,,,,,https://libguides.massgeneral.org/c.php?g=888627,https://libguides.massgeneral.org/c.php?g=888627,,,2948288065,,0,,0,false,,
+022-671-875-867-121,Research Guides: Statistics & Data Sets for Health Care and Public Health: Interdisciplinary Health Data,2016-06-22,2016,libguide,,,,,Elaina Vitale,"Health data sources that intersect with economics, agriculture, energy, society and culture, and other topics.",,,,,Agriculture; Public health; Health care; Political science; Health data; Public relations; Energy (esotericism),,,,,https://researchguides.dartmouth.edu/c.php?g=517073&p=6289151,https://researchguides.dartmouth.edu/c.php?g=517073&p=6289151,,,2914985501,,0,,0,false,,
+025-017-568-985-702,Sharing Health Data,2022-07-31,2022,book,,,National Academies Press,,,,,,,,Data sharing; Computer science; Data science; Medicine; Pathology; Alternative medicine,,,,,,http://dx.doi.org/10.17226/27107,,10.17226/27107,,,0,,2,false,,
+027-240-146-206-486,Libraries Homepage: Health Data & Statistics: Health Data,2019-09-23,2019,libguide,,,,,Mary Cabral,,,,,,Library science; Geography; Health data,,,,,https://libguides.library.clarkson.edu/healthstats,https://libguides.library.clarkson.edu/healthstats,,,2978875515,,0,,0,false,,
+027-401-375-736-119,"Health Reform, Health Data and the Health Information Manager.",2010-06-01,2010,journal article,Health information management : journal of the Health Information Management Association of Australia,18333575; 18333583,Health Information Management Association of Australia,Australia,Kerryn Butler-Henderson,,39,2,7,8,Public health informatics; Health education; Health informatics; HRHIS; Health care; Health promotion; International health; Public relations; Health policy; Medicine,,,,,https://europepmc.org/abstract/MED/28683643 http://ecite.utas.edu.au/100991 http://him.sagepub.com/content/39/2/7.full.pdf https://journals.sagepub.com/doi/pdf/10.1177/183335831003900202 https://espace.curtin.edu.au/handle/20.500.11937/8538 https://www.ncbi.nlm.nih.gov/pubmed/28683643 https://pubmed.ncbi.nlm.nih.gov/28683643/,http://dx.doi.org/10.1177/183335831003900202,28683643,10.1177/183335831003900202,1579350024,,0,000-204-173-798-52X; 008-124-795-557-318; 020-874-425-086-464; 026-725-983-765-559; 044-078-538-020-908; 076-821-224-779-726,5,true,,bronze
+027-851-483-008-061,Health data on data.gov: A research on status quo of open health data,2019-03-15,2019,conference proceedings article,iConference 2019 Proceedings,,iSchools,,Yongyi Wang; Hui Zhang; Dan Wu; Jiangping Chen,,,,,,Political science; Data quality; Status quo; Health data; Environmental health,,,,,https://www.ideals.illinois.edu/handle/2142/103362,http://dx.doi.org/10.21900/iconf.2019.103362,,10.21900/iconf.2019.103362,2927849315,,0,,0,true,,green
+030-243-263-655-24X,Health Data Hub,,2021,journal article,Jusletter,14247410,Weblaw,,Marine Largant; Philipp Fischer,,,1069,,,Business; Health data; Environmental health,,,,,https://jusletter.weblaw.ch/fr/juslissues/2021/1069/health-data-hub_3f6a0d32bb.html,http://dx.doi.org/10.38023/e8410dbc-4db1-4c13-a0b1-344819261b9b,,10.38023/e8410dbc-4db1-4c13-a0b1-344819261b9b,3173857640,,0,,1,false,,
+030-512-632-314-946,Health Data Management,,2008,book chapter,Encyclopedia of Public Health,,Springer Netherlands,,Wolfgang Böcking; Diana Trojanus,,,,544,546,Business,,,,,,http://dx.doi.org/10.1007/978-1-4020-5614-7_1398,,10.1007/978-1-4020-5614-7_1398,,,0,,1,false,,
+030-726-966-252-275,Brisbane Health-y Data: Licensing health and sensitive data,,,,,,,,Olesen Sarah,,,,,,Medical education; Medicine; Environmental health,,,,,https://figshare.com/articles/Brisbane_Health_y_Data_Licencing_health_and_sensitive_data/3119848,http://dx.doi.org/10.6084/m9.figshare.3119848.v2,,10.6084/m9.figshare.3119848.v2,2602175426,,0,,0,false,,
+032-211-407-789-770,Communicating Health Data,2011-04-01,2011,journal article,International public health journal,19474989,,,David E. Nelson; Michael R. Spieker; Bradford W. Hesse,"IntroductionWhatever the purpose, and regardless of whether a health issue arises at the individual or population level, primary health care providers, public health practitioners, and researchers commonly must communicate scientific information, including data (ie, the -numbers"") to lay audiences (1). This is usually hard for many reasons (2-4). It is further complicated by the absence of consensus among scientists and clinicians themselves, which often leads to conflicting interpretations and recommendations; and the abundance of health information now readily available on-line and elsewhere.Clinical and public health situations often occur when communicating scientific data is necessary to try and help ensure that evidence-based research findings are considered and potentially lead to improved health outcomes (1,4). Decisions about disease treatments, immunization policies, cancer screening tests, risks associated with medication, infectious disease outbreaks, and community exposure to potentially hazardous environmental agents represent just a few of many examples where communicating data to lay audiences, including policy makers and patients, is necessary. Fortunately, there is ample research available to guide primary care providers, scientists, and others on how to better select and present data to lay audiences and avoid many of the pitfalls (1,4,5). Data-based decision making is increasingly becoming a key part of medical practice (5-7).The goal of this paper is to provide readers with an overview of communicating health data to lay audiences, with a strong emphasis on applying the OPT-In Framework. How to utilize this framework, described more completely in the book Making Data Talk (1), for the purpose of persuasion (advocacy) to advance policy, and to facilitate informed decision making by individuals in clinical settings, will be demonstrated through two short case studies.BackgroundWhy bother to communicate data to lay audiences?A good starting point is to ask why it is necessary to communicate scientific data. It is true that data do not need to be communicated in some circumstances (1), especially when the purpose for communicating is to instruct people (eg, about how to perform a task or master a skill) or in the event of an emergency. Some people prefer not to receive information in the form of numbers, or more commonly, chose not to accept data that are contradictory with strongly held beliefs, attitudes, values, or behaviors.Nevertheless, the main reason to communicate data is that lay audiences want to know the reasons why they should believe or do what health care researchers and practitioners recommend (1,4,8). For example, why should I receive periodic colorectal cancer screening tests beginning at age 50? Why should tobacco excise taxes be increased? In the current internet era, and with greater involvement of people in their own health care decision making, clinical and public health care providers need to be prepared to answer such questions.Failing to communicate important findings, or communicating them poorly, especially when there is scientific consensus, prevents scientific evidence from having the chance to help improve the health of lay audiences. Data can provide evidence to justify conclusions or recommendations of scientists, that is, rationales (9-11). In many situations, communicating data to lay audiences can increase knowledge, facilitate informed decision making, and be persuasive (12-14). In clinical settings, for example, the way in which data are communicated can affect the emotional responses of patients, adherence to recommendations, and their own sense of personal efficacy (1,15).Common challengesThere are many reasons why communicating data to lay audiences is difficult. To begin with, national surveys in the United States and studies in other developed countries confirm that mathematical (quantitative) and scientific literacy among adults is surprisingly low (1,16). …",3,2,151,,The Internet; Scientific evidence; Public health; Nursing; Health care; Scientific consensus; Persuasion; Public relations; Event (computing); Medicine; Scientific literacy,,,,,,,,,2746626683,,0,,1,false,,
+033-991-130-809-076,Big Data for Health,2015-07-10,2015,journal article,IEEE journal of biomedical and health informatics,21682208; 21682194,Institute of Electrical and Electronics Engineers (IEEE),United States,Javier Andreu-Perez; Carmen C. Y. Poon; Robert Merrifield; Stephen T. C. Wong; Guang-Zhong Yang,"This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.",19,4,1193,1208,Health Administration Informatics; Health informatics; Engineering informatics; Translational bioinformatics; Informatics; Data science; Imaging informatics; Materials informatics; Medicine; Big data,,"Computational Biology; Databases, Factual; Diagnostic Imaging; Humans; Medical Informatics",,,https://dblp.uni-trier.de/db/journals/titb/titb19.html#PerezPMWY15 https://www.mendeley.com/catalogue/81f93580-0994-3668-83da-861ae701edd9/ https://pubmed.ncbi.nlm.nih.gov/26173222/ http://dx.doi.org/10.1109/JBHI.2015.2450362 https://europepmc.org/abstract/MED/26173222 http://www.cs.helsinki.fi/u/jilu/paper/bigdataapplication02.pdf https://scholars.houstonmethodist.org/en/publications/big-data-for-health https://dx.doi.org/10.1109/JBHI.2015.2450362 http://ieeexplore.ieee.org/document/7154395/ http://dblp.uni-trier.de/db/journals/titb/titb19.html#PerezPMWY15 https://scholars.houstonmethodist.org/en/publications/big-data-for-health(6ce39895-666f-4138-9754-3302e41e6244).html https://www.ncbi.nlm.nih.gov/pubmed/26173222 https://doi.org/10.1109/JBHI.2015.2450362 https://ieeexplore.ieee.org/abstract/document/7154395/,http://dx.doi.org/10.1109/jbhi.2015.2450362,26173222,10.1109/jbhi.2015.2450362,1541250240,,1,001-159-184-276-045; 001-237-107-392-238; 002-127-514-713-907; 002-882-189-607-594; 003-348-455-733-996; 003-919-392-776-783; 003-960-105-380-968; 004-003-672-553-687; 004-806-173-117-716; 005-027-099-637-505; 006-227-410-803-151; 006-385-937-065-275; 007-209-903-937-024; 007-730-712-196-101; 008-099-874-361-071; 008-311-358-924-099; 008-574-715-689-643; 008-975-998-423-892; 009-531-378-283-656; 009-885-842-178-277; 010-445-571-877-83X; 011-039-141-585-864; 012-621-209-567-639; 012-742-041-115-014; 013-369-213-457-963; 013-412-979-942-349; 013-449-921-070-874; 013-514-353-200-455; 013-532-299-475-886; 013-662-727-978-128; 013-937-941-060-334; 015-150-379-939-328; 015-321-995-197-333; 015-523-047-624-230; 015-648-955-081-381; 015-728-667-810-180; 016-085-778-792-763; 016-287-951-038-269; 017-232-675-682-346; 017-286-662-456-108; 017-694-151-203-312; 018-478-883-712-542; 019-852-438-343-148; 020-174-844-068-896; 021-029-559-327-639; 021-902-790-186-65X; 022-940-313-039-79X; 026-199-247-038-495; 026-255-701-367-98X; 026-419-799-073-776; 026-843-906-841-324; 028-009-912-077-519; 028-734-407-885-832; 029-166-900-996-37X; 029-707-459-707-90X; 030-070-019-376-668; 030-726-472-613-317; 031-864-417-581-772; 032-331-972-310-124; 032-356-022-322-667; 032-812-489-885-128; 033-605-013-293-105; 033-680-133-346-283; 033-692-603-343-406; 033-753-266-989-199; 034-047-656-668-022; 034-337-831-637-975; 035-172-298-680-37X; 035-703-451-955-094; 035-846-740-268-010; 037-748-325-586-541; 038-694-567-765-644; 039-800-893-787-18X; 040-223-240-731-363; 040-918-384-030-604; 041-260-888-191-872; 041-949-819-223-340; 042-696-797-334-86X; 042-888-961-521-664; 043-948-939-296-462; 044-319-191-393-856; 044-675-860-680-766; 044-937-461-587-924; 046-162-436-292-27X; 047-914-834-424-798; 049-465-955-189-837; 050-493-685-146-573; 052-883-184-951-749; 053-064-322-259-695; 053-190-290-676-183; 053-492-676-246-745; 054-328-982-724-479; 055-489-014-149-087; 055-754-564-537-054; 056-940-627-823-636; 057-159-740-034-568; 057-567-861-197-788; 059-808-864-096-023; 059-945-342-211-865; 060-999-459-909-385; 061-093-604-168-751; 063-729-686-182-621; 063-886-871-420-061; 064-865-801-324-81X; 065-370-775-893-358; 066-554-630-102-431; 066-621-704-315-198; 067-382-356-300-264; 067-584-535-199-291; 071-736-895-445-008; 071-879-937-874-037; 072-190-224-983-256; 074-780-353-373-211; 075-497-541-068-516; 077-755-204-641-213; 081-626-963-670-115; 081-642-793-280-674; 082-118-133-190-628; 082-274-054-270-018; 082-888-465-860-369; 085-640-372-939-879; 087-755-996-169-027; 090-235-975-575-332; 092-636-827-306-288; 094-661-422-371-468; 099-913-928-137-599; 101-174-159-425-118; 102-282-851-300-308; 107-298-495-808-292; 107-872-522-684-920; 110-591-814-449-532; 111-000-137-351-86X; 113-747-653-833-249; 114-091-055-154-075; 116-687-838-487-319; 117-604-351-033-518; 118-767-528-966-907; 118-949-481-103-609; 120-550-775-904-337; 120-827-721-750-044; 127-134-528-683-700; 129-777-405-649-678; 141-579-272-068-841; 170-088-403-247-168; 178-841-402-537-081; 186-883-938-445-789; 192-117-215-641-318,553,true,implied-oa,hybrid
+035-542-736-493-941,Data-Reproductive Health,,,,,,,,Servan-mori Edson,,,,,,Health education; Public health; Health care; Health promotion; Occupational safety and health; Occupational health nursing; Global health; Health policy; Medicine; Environmental health,,,,,https://figshare.com/articles/Data_Reproductive_Health/3121678/1,http://dx.doi.org/10.6084/m9.figshare.3121678.v1,,10.6084/m9.figshare.3121678.v1,2595144144,,0,,0,false,,
+036-842-302-145-799,Visualization of Health Data,,2019,book chapter,Communications in Computer and Information Science,18650929; 18650937,Springer Singapore,Germany,Veronica Castro Alvarez; Ching-yu Huang,"As data becomes more accessible, visualization methods are needed to help make sense of the information. Analyzing and visualizing data helps the public to better recognize the patterns and connections between different datasets. By using visual elements such as graphs, charts, and maps, it is easier to see and understand the trends and outliers in data. This project aims to study the correlation between environmental factors and public health. Large sets of data pertaining to the environment and health were gathered from open data sources. The tool used to analyze and visualize the collected data is Tableau, which is a software program that is used to transform data into dashboards and visuals such as treemaps, histograms, or area charts. For this project, the data will be displayed through charts and interactive maps that will be created through this software.",,,118,130,Information retrieval; Software; Open data; Health data; Visualization methods; Visualization; Computer science; Histogram; Outlier,,,,,https://link.springer.com/chapter/10.1007%2F978-981-15-1758-7_10 https://rd.springer.com/chapter/10.1007/978-981-15-1758-7_10,http://dx.doi.org/10.1007/978-981-15-1758-7_10,,10.1007/978-981-15-1758-7_10,2996862293,,0,007-569-931-508-701; 011-688-586-824-549; 013-477-691-652-104; 019-870-073-237-244; 064-162-756-041-173; 065-380-297-242-591; 086-477-688-292-126; 098-553-995-906-244,0,false,,
+037-110-767-701-073,Targeting health data,,2019,journal article,Computer Fraud & Security,13613723; 18737056,Mark Allen Group,United Kingdom,,"<jats:p> Concerns are being raised about Google's recent moves to acquire vast quantities of health data. In the meantime, cyber criminals are also targeting healthcare organisations and the data they hold. </jats:p>",2019,12,3,3,Computer science; Business; Internet privacy,,,,,,http://dx.doi.org/10.1016/s1361-3723(19)30123-x,,10.1016/s1361-3723(19)30123-x,,,0,,0,false,,
+041-088-173-402-38X,Digital Health Data and Data Security,2021-03-22,2021,book chapter,The Digital Pill: What Everyone Should Know about the Future of Our Healthcare System,,Emerald Publishing Limited,,Elgar Fleisch; Christoph Franz; Andreas Herrmann,,,,99,120,Data security; Digital health; Computer security; Computer science,,,,,https://www.emerald.com/insight/content/doi/10.1108/978-1-78756-675-020211007/full/html,http://dx.doi.org/10.1108/978-1-78756-675-020211007,,10.1108/978-1-78756-675-020211007,3133879123,,0,,0,false,,
+041-877-716-924-494,Data Performativity and Health: The Politics of Health Data Practices in Europe:,2019-10-20,2019,journal article,"Science, Technology, & Human Values",01622439; 15528251,SAGE Publications,United States,Gabriel G. Blouin,"The European Commission produces the European Core Health Indicators (ECHI), a database containing different tools used to compare European Union (EU) countries and recommend policy changes. The EC...",45,2,317,341,Health indicator; Political science; Core (game theory); Performativity; European union; Health data; European commission; Public administration; Politics,,,,,http://journals.sagepub.com/doi/10.1177/0162243919882083 https://journals.sagepub.com/doi/full/10.1177/0162243919882083,http://dx.doi.org/10.1177/0162243919882083,,10.1177/0162243919882083,2981541947,,0,001-598-392-459-537; 003-055-332-115-216; 003-940-253-529-519; 005-883-538-394-35X; 016-406-702-841-981; 020-122-356-920-328; 024-507-935-423-548; 026-728-673-180-065; 027-961-615-297-22X; 028-667-977-380-548; 032-289-606-428-281; 038-688-112-403-574; 041-966-858-133-318; 045-763-131-605-033; 049-368-274-783-22X; 050-018-549-937-474; 052-716-532-265-641; 054-488-659-518-395; 057-133-549-287-377; 065-967-638-758-573; 068-497-046-717-554; 071-038-889-109-86X; 073-672-142-508-206; 074-552-868-080-974; 074-856-220-086-671; 079-099-789-318-971; 082-790-951-501-438; 083-237-491-234-801; 085-264-553-411-241; 096-324-507-622-380; 104-433-234-104-238; 106-269-158-530-059; 110-703-509-422-396; 118-468-302-740-200; 121-231-441-109-729; 124-015-416-773-014; 125-908-128-673-590; 132-283-605-571-762; 147-705-334-986-318; 162-963-535-109-624; 168-212-329-955-302; 178-371-659-156-352,5,false,,
+044-370-256-324-40X,Guides: Health Data & Statistics: Health Data,2014-10-09,2014,libguide,,,,,Denise Smith,,,,,,Geography; Health data; Environmental health,,,,,https://hsl.mcmaster.libguides.com/data https://hslmcmaster.libguides.com/data http://hsl.mcmaster.libguides.com/data,https://hslmcmaster.libguides.com/data,,,1199852055,,0,,0,false,,
+044-403-309-684-723,BIG HEALTH DATA COMBINED WITH SMALL HEALTH DATA—A FRAMEWORK FOR A PERSONAL HEALTH DATA BANK,2017-06-30,2017,journal article,Innovation in Aging,23995300,Oxford University Press (OUP),,Mike Martin,,1,suppl_1,1313,1313,Data science; Data bank; Personal health; Health data; Medicine,,,,,http://europepmc.org/articles/PMC6183886 https://academic.oup.com/innovateage/article/1/suppl_1/1313/3902047,http://dx.doi.org/10.1093/geroni/igx004.4812,,10.1093/geroni/igx004.4812,2728922397,,0,,2,true,"CC BY, CC BY-NC-ND",gold
+046-280-490-899-141,Health Information Governance in a Digital Environment - Health data and data governance.,,2013,journal article,Studies in health technology and informatics,18798365; 09269630,IOS Press,Netherlands,Evelyn J. S. Hovenga; Heather Grain,"Health is a knowledge industry, based on data collected to support care, service planning, financing and knowledge advancement. Increasingly there is a need to collect, retrieve and use health record information in an electronic format to provide greater flexibility, as this enables retrieval and display of data in multiple locations and formats irrespective of where the data were collected. Electronically maintained records require greater structure and consistency to achieve this. The use of data held in records generated in real time in clinical systems also has the potential to reduce the time it takes to gain knowledge, as there is less need to collect research specific information, this is only possible if data governance principles are applied. Connected devices and information systems are now generating huge amounts of data, as never before seen. An ability to analyse and mine very large amounts of data, ""Big Data"", provides policy and decision makers with new insights into varied aspects of work and information flow and operational business patterns and trends, and drives greater efficiencies, and safer and more effective health care. This enables decision makers to apply rules and guidance that have been developed based upon knowledge from many individual patient records through recognition of triggers based upon that knowledge. In clinical decision support systems information about the individual is compared to rules based upon knowledge gained from accumulated information of many to provide guidance at appropriate times in the clinical process. To achieve this the data in the individual system, and the knowledge rules must be represented in a compatible and consistent manner. This chapter describes data attributes; explains the difference between data and information; outlines the requirements for quality data; shows the relevance of health data standards; and describes how data governance impacts representation of content in systems and the use of that information.",193,,67,92,Information flow (information theory); Enterprise data management; Health care; Information system; Information governance; Medical record; Data governance; Data quality; Data science; Computer science; Clinical decision support system; Big data; Relevance (information retrieval),,"Data Collection/methods; Data Mining/methods; Government Regulation; Health Information Systems/organization & administration; Medical Informatics/organization & administration; Models, Organizational; National Health Programs/organization & administration; Needs Assessment/organization & administration; Public Policy; World Health Organization/organization & administration",,,https://ebooks.iospress.nl/publication/35106 https://pubmed.ncbi.nlm.nih.gov/24018511/ https://www.ncbi.nlm.nih.gov/pubmed/24018511 https://doi.org/10.3233/978-1-61499-291-2-67,https://www.ncbi.nlm.nih.gov/pubmed/24018511,24018511,,2405842082,,0,,5,false,,
+046-603-665-006-821,"Big Data, Algorithms and Health Data",,2019,journal article,SSRN Electronic Journal,15565068,Elsevier BV,,Julia M. Puaschunder,,,,,,Algorithm; Business; Health care; Data breach; Inclusive growth; Liability; European union; Incentive; Big data; Property rights,,,,,https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3474126,http://dx.doi.org/10.2139/ssrn.3474126,,10.2139/ssrn.3474126,2990297356,,0,,3,false,,
+046-866-773-043-050,Insights in Public Health: For the Love of Data! The Hawai'i Health Data Warehouse.,,2015,journal article,Hawai'i journal of medicine & public health : a journal of Asia Pacific Medicine & Public Health,21658242; 21658218,"University Clinical, Education & Research Associates (UCERA)",United States,Chosy J; Benson K; Belen D; Starr R; Lowery St John T; Starr Rr; Lance K Ching,"Data form the framework around which important public health decisions are made. Public health data are essential for surveillance and evaluating change. In Hawai‘i, public health data come from a multitude of sources and agencies. The Hawai‘i Health Data Warehouse (HHDW) was created to pull those data into a single location and to present results in a form that is easy for the public to access and utilize. In the years since its creation, HHDW has built a second consumer-focused web site, Hawai‘i Health Matters, and is now introducing new functionality on the original site that allows users to define their own enquiry. The newly adopted Indicator-Based Information System (IBIS) uses a web interface to perform real-time data analysis and display results. This gives users the power to examine health data by a wide range of demographic and socioeconomic dimensions, permitting them to pinpoint the data they need.",74,11,382,385,World Wide Web; Public health; Health informatics; Socioeconomic status; Information system; Multitude; Ibis; Health data; Computer science; User interface,BRFSS; Indicator-based information system; PRAMS; Public health data; Vital statistics; YRBS,Hawaii; Humans; Medical Informatics/statistics & numerical data; Public Health/statistics & numerical data,,,http://europepmc.org/articles/PMC4642500 https://www.ncbi.nlm.nih.gov/pubmed/26568903,https://www.ncbi.nlm.nih.gov/pubmed/26568903,26568903,,2415063887,PMC4642500,0,,1,true,,unknown
+047-938-110-683-803,Textbook of Global Health - Data on Health,2017-04-01,2017,book chapter,Textbook of Global Health,,Oxford University Press,,Anne-Emanuelle Birn; Yogan Pillay; Timothy H. Holtz,,,,193,230,Public health informatics; Health indicator; Business; HRHIS; Environmental health,,,,,,http://dx.doi.org/10.1093/acprof:oso/9780199392285.003.0005,,10.1093/acprof:oso/9780199392285.003.0005,2601276174,,0,,0,false,,
+053-943-268-330-577,Health Data,,2008,book chapter,Encyclopedia of Public Health,,Springer Netherlands,,,,,,544,544,Computer science,,,,,,http://dx.doi.org/10.1007/978-1-4020-5614-7_1397,,10.1007/978-1-4020-5614-7_1397,,,0,,0,false,,
+054-094-890-945-566,LibGuides: Health Economics: Health Data,2014-08-21,2014,libguide,,,,,Chris Palazzolo,,,,,,Public economics; Political science; Health data; Health economics,,,,,https://guides.main.library.emory.edu/c.php?g=129678&p=846827 https://guides.libraries.emory.edu/c.php?g=129678&p=846827,https://guides.libraries.emory.edu/c.php?g=129678&p=846827,,,477716411,,0,,0,false,,
+054-923-951-621-609,Personal health data,,2014,,,,,,Mathias Funk; P.P.M. Fens,,,,,,Psychology; Personal health; Personal experience; Medical education,,,,,,,,,2529860925,,0,,0,false,,
+055-484-857-995-49X,Health Services Data: Typology of Health Care Data,2019-02-12,2019,book chapter,Health Services Evaluation,25118293; 25118307,Springer US,,Ross M. Mullner,"Health services researchers study access, cost, quality, and the outcome of health care. These researchers frequently use existing data collected by government agencies and private organizations to monitor and evaluate current health care programs and systems and to predict the consequences of proposed new health policies. Primarily focusing on US data sources, this chapter outlines a practical typology, or classification framework, of health care data that is often used by these researchers when they are gathering data and conducting their studies. The typology addresses three important inextricably linked questions. First, what is the basic unit of analysis for the study? These units include individuals, households, groups/populations, health care organizations, health care programs, and national health care systems. Second, how were these data collected? The methods used to collect data include literature reviews, observations, focus groups, surveys, medical records and administrative and billing sources, registries, and vital records. Third, which government agency or private organization collected and is currently holding these data? Government data collection and holding agencies include US health information clearinghouses and libraries, US registries, US government agencies and departments, health programs and systems of other (non-US) nations, and government sponsored international organizations. Private data collecting and holding organizations include health information clearinghouses and libraries; accreditation, evaluation, and regulatory organizations; associations and professional societies; foundations and trusts; health insurance and employee benefits organizations; registries; research and policy organizations; and survey research organizations. To illustrate each of the questions and classifications, many examples are provided and discussed. And many US and other public use data files are identified and described.",,,77,108,Health care; Typology; Government (linguistics); Agency (philosophy); Accreditation; Business; HRHIS; Public relations; Data collection; Health policy; Medicine; Political science; Medical education; Sociology; Law; Social science; Linguistics; Philosophy; Anthropology,,,,,,http://dx.doi.org/10.1007/978-1-4939-8715-3_6,,10.1007/978-1-4939-8715-3_6,,,0,000-366-208-221-985; 002-527-515-183-162; 022-789-077-524-70X; 025-798-701-183-114; 032-192-698-078-790; 035-900-905-291-417; 037-951-253-812-40X; 061-788-469-784-033; 065-096-116-210-040; 070-130-003-275-013; 074-275-980-004-055; 094-428-072-929-28X; 142-660-672-050-717; 147-260-852-741-995; 164-979-246-230-785; 191-440-264-563-241,1,false,,
+059-182-108-354-857,Health data justice: building new norms for health data governance.,2023-02-28,2023,journal article,NPJ digital medicine,23986352,Springer Science and Business Media LLC,England,James Shaw; Sharifah Sekalala,"The retention and use of health-related data by government, corporate, and health professional actors risk exacerbating the harms of colonial systems of inequality in which health care and public health are situated, regardless of the intentions about how those data are used. In this context, a data justice perspective presents opportunities to develop new norms of health-related data governance that hold health justice as the primary objective. In this perspective, we define the concept of health data justice, outline urgent issues informed by this approach, and propose five calls to action from a health data justice perspective.",6,1,30,,Economic Justice; Corporate governance; Public health; Government (linguistics); Public relations; Context (archaeology); Perspective (graphical); Data governance; Health care; Health equity; Political science; Sociology; Public administration; Business; Medicine; Data quality; Nursing; Computer science; Law; Geography; Linguistics; Philosophy; Archaeology; Finance; Marketing; Artificial intelligence; Metric (unit),,,,Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada) (102862),,http://dx.doi.org/10.1038/s41746-023-00780-4,36854964,10.1038/s41746-023-00780-4,,PMC9972302,0,001-571-126-536-966; 003-092-235-403-69X; 006-207-355-449-138; 006-751-990-123-670; 013-689-877-115-790; 015-879-643-920-586; 016-563-949-452-67X; 038-190-475-545-135; 039-198-288-017-508; 044-413-384-862-293; 045-701-770-083-717; 045-704-721-406-309; 054-188-599-676-011; 069-538-187-965-193; 086-989-786-913-95X; 095-151-038-932-204; 105-256-769-558-211; 110-816-194-130-196; 118-526-764-523-545; 130-857-098-546-462; 143-351-242-157-386; 188-727-007-170-076,5,true,cc-by,gold
+059-321-149-260-846,Data policies for big health data and personal health data,,2016,dissertation,,,,,Pepukayi David Junior Chitondo,,,,,,Internet privacy; Business; Health informatics; Personal information management; Personal health; Health data; Big data,,,,,http://etd.cput.ac.za/handle/20.500.11838/2479 http://etd.cput.ac.za/bitstream/20.500.11838/2479/1/210227338-Chitondo-Pepukayi-David-Junior-Mtech-Information-Technology-FID-2017.pdf,http://etd.cput.ac.za/handle/20.500.11838/2479,,,2800283888,,0,000-881-658-602-554; 001-366-131-837-824; 003-652-274-366-732; 005-418-156-652-348; 006-907-491-457-711; 010-144-765-186-560; 010-638-271-971-534; 011-073-064-042-354; 013-119-111-595-159; 017-058-895-069-681; 017-167-454-912-226; 017-254-834-609-478; 018-586-714-061-150; 020-075-642-556-724; 020-735-974-503-478; 020-884-523-153-420; 020-994-840-357-15X; 026-419-799-073-776; 037-213-022-702-823; 037-550-015-414-716; 039-565-476-988-610; 041-281-966-868-002; 046-139-599-112-389; 047-290-850-326-714; 053-445-152-187-56X; 055-895-840-389-529; 060-871-414-825-628; 066-189-151-904-901; 069-425-627-990-261; 071-073-095-647-571; 076-857-235-195-130; 078-209-127-067-665; 084-662-845-086-29X; 096-878-885-502-575; 097-065-189-477-730; 101-411-013-473-092; 106-177-825-389-602; 108-393-007-630-459; 108-618-903-572-554; 110-756-842-556-338; 115-822-602-969-672; 120-858-452-897-954; 121-990-114-696-118; 141-369-560-564-043; 141-706-104-681-064; 155-929-896-771-573; 172-282-312-128-202; 186-449-841-475-277; 189-128-072-372-688; 194-176-472-098-806; 196-343-075-300-941,0,false,,
+060-099-550-787-725,Introduction to Texas health data: A public health data resource,2019-11-05,2019,,,,,,Jessica D. Cance,,,,,,Public health; Business; Resource (biology); Health data; Environmental planning,,,,,,,,,3171547629,,0,,0,false,,
+060-563-483-761-07X,FAIR Health Data and HL7 FHIR in the European Health Data Space,2021-09-04,2021,journal article,Applied Medical Informatics,12245593,,,Catherine E. Chronaki,,43,,2,2,Data science; Space (commercial competition); Health data; Computer science,,,,,https://ami.info.umfcluj.ro/index.php/AMI/article/download/880/811 https://ami.info.umfcluj.ro/index.php/AMI/article/view/880,https://ami.info.umfcluj.ro/index.php/AMI/article/view/880,,,3199195657,,0,,0,true,cc-by,gold
+061-319-137-556-614,Health Data,2017-09-20,2017,book chapter,Essential Introduction to Understanding European Data Protection Rules,,Auerbach Publications,,Paul B. Lambert,,,,385,391,,,,,,,http://dx.doi.org/10.1201/9781138069848-36,,10.1201/9781138069848-36,,,0,,0,false,,
+062-412-814-931-403,Health Informatics Data Analysis - Health Informatics Data Analysis,,2017,book,Health Information Science,23660988; 23660996,Springer International Publishing,,Dong Xu; May D. Wang; Fengfeng Zhou; Yunpeng Cai,,,,,,Public health informatics; Engineering; Health Administration Informatics; Health informatics; Translational bioinformatics; Medical education; Translational research informatics,,,,,http://link.springer.com/10.1007/978-3-319-44981-4,http://dx.doi.org/10.1007/978-3-319-44981-4,,10.1007/978-3-319-44981-4,2754197982,,0,,0,false,,
+067-550-145-457-229,Statistics and Health Data,,2020,book chapter,Springer Texts in Statistics,1431875x; 21974136,Springer International Publishing,,Ruth Etzioni; Micha Mandel; Roman Gulati,"The evolution of health care data resources is creating vast new opportunities for population health research. This text is designed to guide public health students and practitioners as they navigate this changing landscape to develop competence as health data analysts. In this chapter, we define the concept of organic statistics, which will form a foundation for the methods presented in this and subsequent chapters. We differentiate between hypothesis-driven research based on classical conceptual models and predictive analytics methods that are more data-driven. We introduce the most common types of publicly available health data, and we provide examples of the types of real-world research questions that will be featured throughout the text. This chapter thus creates a roadmap for forthcoming chapters while standing alone as an introduction to the key themes of this text: health data resources and their features, the research question and its role in analysis, and the mindset of organic statistics.",,,1,16,Competence (human resources); Statistics; Public health; Sociology; Health care; Population health; Mindset; Research question; Foundation (evidence); Predictive analytics,,,,,https://link.springer.com/chapter/10.1007/978-3-030-59889-1_1,http://dx.doi.org/10.1007/978-3-030-59889-1_1,,10.1007/978-3-030-59889-1_1,3120457797,,0,000-550-668-931-312; 014-099-018-140-951; 033-878-186-999-134; 034-157-959-944-837; 035-512-982-350-877; 051-069-849-628-880; 053-942-378-056-711; 055-170-093-730-86X; 094-486-438-255-618; 114-251-883-659-335; 132-968-585-498-554; 135-409-870-665-532; 159-664-151-777-422; 176-823-274-830-387,0,false,,
+069-606-639-161-981,Accessing Health Data,2017-03-16,2017,book chapter,Improving Population Health Using Electronic Health Records,,CRC Press,,,,,,37,49,Computer science,,,,,,http://dx.doi.org/10.1201/9781315153100-4,,10.1201/9781315153100-4,,,0,,0,false,,
+070-619-479-313-20X,Guides: Data: Health Data,2011-04-18,2011,,,,,,Kaelan Caspary,,,,,,Geography; Data science; Health data,,,,,,,,,3135379271,,0,,0,false,,
+071-495-372-541-75X,Econometric Analysis of Health Data - Econometric analysis of health data,2002-04-30,2002,book,,,Wiley,,Andrew M. Jones; Owen O'Donnell,"List of Contributors. Preface. Introduction (Andrew M. Jones and Owen Oa Donnell). PART I: LATENT VARIABLES AND SELECTION PROBLEMS. The demand for health: an empirical reformulation of the Grossman model. (Adam Wagstaff). Health, health care and the environment: Econometric evidence from German micro data.(Manfred Erbsland, Walter Ried and Volker Ulrich). Subjective health measures and state dependent reporting errors. (Marcel Kerkhofs and Maarten Lindeboom.) The effect of smoking on health using a sequential self--selection model. (Kajal Lahiri and Jae G. Song). PART II: COUNT DATA AND SURVIVAL ANALYSIS. A comparison of alternative models of prescription drug utilization. (Paul V. Grootendorst). Estimates of the use and costs of behavioural health care: a comparison of standard and finite mixture models. (Partha Deb and Ann M. Holmes). An empirical analysis of the demand for physician services across the European Union. (Sergi Jimenez--Martin, Jose M. Labeaga, Maite Martinez--Granado). Proportional treatment effects for count response panel data: Effects of binary exercise on health care demand. (Myoung--jae Lee and Saturo Kobayashi). Estimating surgical volume--outcome relationships applying survival models: accounting for frailty and hospital fixed effects. (Barton H. Hamilton and Vivian H. Ho). PART III: FLEXIBLE AND SEMIPARAMETIC ESTIMATORS. Individual cigarette consumption and addiction: a flexible limited dependent variable approach. (Steven T. Yen and Andrew M. Jones). Identifying demand for health resources using waiting times information. (Richard Blundell and Frank Windmeijer). Non-- and semiparametric estimation of age and time heterogeneity in repeated cross--sections: an application to self--reported morbidity and general practitioner utilisation. (David Parkin, Nigel Rice and Matthew Sutton). PART IV: CLASSICAL AND SIMULATION METHODS FOR PANEL DATA. Unobserved heterogeneity and censoring in the demand for health care. (Angel Lopez--Nicolas). A discrete random effects probit model with application to the demand for preventive care. (Partha Deb). The use of long--term care services by the Dutch elderly. (France Portrait, Maarten Lindeboom and Dorly Deeg). HMO selection and medical care costs: Bayesian MCMC estimation of a robust panel data probit model with survival. (Barton H. Hamilton). Index.",,1,,,Medical statistics; Econometrics; Health care; Random effects model; Econometric model; European union; Latent variable; Panel data; Probit model; Medicine,,,,,http://ci.nii.ac.jp/ncid/BA56869147,http://dx.doi.org/10.1002/0470846313,,10.1002/0470846313,379579685,,0,,45,true,,bronze
+074-947-381-920-421,Health quality data.,1996-09-01,1996,journal article,Cleveland Clinic journal of medicine,08911150; 19392869,Cleveland Clinic Journal of Medicine,United States,John C. Morley; Dwain L. Harper,,63,5,303,304,MEDLINE; Health quality; Medical education; Text mining; Medicine,,Health Maintenance Organizations/standards; Humans; Ohio; Quality of Health Care/standards,,,https://www.ccjm.org/content/63/5/303 https://www.ncbi.nlm.nih.gov/pubmed/8870342 https://www.ccjm.org/content/ccjom/63/5/303.full.pdf http://europepmc.org/abstract/MED/8870342,http://dx.doi.org/10.3949/ccjm.63.5.303,8870342,10.3949/ccjm.63.5.303,2430965200,,0,,0,false,,
+080-284-263-514-177,Health effects data,,1985,journal article,Journal of the Air Pollution Control Association,00022470,Air Pollution Control Association,United States,S H Lamm,,35,5,454,455,MEDLINE; Medicine; Environmental health,,Air Pollution/adverse effects; Humans; Japan; Photochemistry,,,https://www.ncbi.nlm.nih.gov/pubmed/4008748 https://jhu.pure.elsevier.com/en/publications/health-effects-data-5 https://pubmed.ncbi.nlm.nih.gov/4008748/,https://www.ncbi.nlm.nih.gov/pubmed/4008748,4008748,,2409267507,,0,035-681-157-859-654; 062-500-139-018-951,0,false,,
+080-327-461-319-601,Health data resources.,,2002,journal article,Bulletin of the American College of Surgeons,00028045,American College of Surgeons,United States,Cynthia Kay Sykes,,87,9,8,11,HRHIS; Data science; Health data; Text mining; Medicine,,Directories as Topic; Internet; Medical Informatics; United States,,,https://www.ncbi.nlm.nih.gov/pubmed/17387843 https://europepmc.org/article/MED/17387843,https://www.ncbi.nlm.nih.gov/pubmed/17387843,17387843,,2401265329,,0,020-708-943-821-920; 023-493-374-180-994; 025-204-689-251-258; 044-526-531-087-233; 115-989-756-383-417; 124-490-229-182-22X,0,false,,
+082-341-402-343-496,Health data for all.,2022-05-03,2022,journal article,Nature,14764687; 00280836,Springer Science and Business Media LLC,United Kingdom,Jyoti Madhusoodanan,"Medical records can be tricky to access because of confidentiality and variability, but data-sharing efforts are helping to overcome these hurdles — without compromising patient privacy. Medical records can be tricky to access because of confidentiality and variability, but data-sharing efforts are helping to overcome these hurdles — without compromising patient privacy.",605,7908,182,183,Confidentiality; Data sharing; Internet privacy; Medical record; Patient confidentiality; Patient data; Health data; Data access; Computer science; Data science; Computer security; Business; Health care; Medicine; Alternative medicine; Political science; Database; Law; Pathology; Radiology,Databases; Medical research; Research data; SARS-CoV-2,,,,,http://dx.doi.org/10.1038/d41586-022-01205-0,35505184,10.1038/d41586-022-01205-0,,,0,,3,true,,bronze
+082-531-782-262-141,Data for health planning.,,1980,journal article,Times,,,United States,Martin Cd; Taylor P; Van Cleave Ml; Plumlee G,,21,3,34,37,Business; HRHIS; Health planning; Environmental planning,,Data Collection; Health Planning; Health Status; Health Workforce; Hospitalization; Population; Tennessee; United States; Vital Statistics,,,https://www.ncbi.nlm.nih.gov/pubmed/10245766,https://www.ncbi.nlm.nih.gov/pubmed/10245766,10245766,,2410848985,,0,,0,false,,
+083-444-727-254-772,Big health data,,2017,journal article,New Scientist,02624079; 13561766; 20595387; 13648500,Elsevier BV,United States,,,234,3123,4,5,Big data; Business; Computer science; Data science; Data mining,,,,,,http://dx.doi.org/10.1016/s0262-4079(17)30798-4,,10.1016/s0262-4079(17)30798-4,,,0,,0,false,,
+083-675-859-415-257,MGH Guides: Health Data Sources: Global Health Data,2018-11-01,2018,libguide,,,,,Amanda Tarbet,,,,,,Business; Health data; Global health; Environmental health,,,,,https://libguides.massgeneral.org/c.php?g=888627&p=7082192,https://libguides.massgeneral.org/c.php?g=888627&p=7082192,,,2996307498,,0,,0,false,,
+085-648-747-909-294,What is Health Data?,2022-02-17,2022,journal article,Communication and Medicine,16133625; 16121783,Equinox Publishing,United Kingdom,Claus Rehfeld; Melanie E. Kreye; Helena Goldstein Wendelboe; Tove Holm-Larsen,"<jats:p>‘Health Data’ is a term that is used in many different contexts, but understandings of what it encompasses are at best vague. Without an agreed definition, effective law making, ethical discussions and the development of solutions that relate to Health Data are hindered, and decisions about how and when it can be utilized will be distorted and inconsistent, meaning that the potential value of this important resource for society will not be realized. This study contributes to the healthcare literature by offering an empirical characterization of Health Data, enabling a more rigorous and informed discussion through an exploration of its characteristics and how these can support the formulation of a definition which is functional at an interdisciplinary level.Qualitative interviews with 30 Danish stakeholders working with data and health indicate that a proper definition of Health Data should acknowledge a distinction between when the focus is on the source of the data and when it is on how data is used. Further, it needs to incorporate information relating both to clinical data involving patients and to a population’s health status and behaviors more generally. Lastly, it needs to encompass structural data, pertaining to the health system and to wider societal and environmental factors.</jats:p>",,,,,Meaning (existential); Resource (disambiguation); Health care; Danish; Value (mathematics); Data science; Psychology; Population health; Population; Management science; Public relations; Computer science; Medicine; Political science; Environmental health; Engineering; Computer network; Linguistics; Philosophy; Machine learning; Law; Psychotherapist,,,,,,http://dx.doi.org/10.1558/cam.17951,,10.1558/cam.17951,,,0,,0,false,,
+087-129-405-390-224,Health data spread,,2016,journal article,New Scientist,02624079; 13561766; 20595387; 13648500,Elsevier BV,United States,,,229,3064,6,6,Medicine,,,,,,http://dx.doi.org/10.1016/s0262-4079(16)30385-2,,10.1016/s0262-4079(16)30385-2,,,0,,0,false,,
+088-494-306-761-941,Data in–data out? A metasynthesis of interpreter’s experiences in health and mental health:,2017-04-28,2017,journal article,Qualitative Social Work,14733250; 17413117,SAGE Publications,United States,Alice G Yick; Andrea M Daines,The goal of this metasynthesis study was to employ qualitative research studies to explore the experiences of interpreters working in hospitals or mental healthcare facilities. Using Noblit and Har...,18,1,98,115,Interpreter; Mental health; Nursing; Social work; Qualitative research; Mental healthcare; Medicine,,,,,http://journals.sagepub.com/doi/full/10.1177/1473325017707027 https://journals.sagepub.com/doi/10.1177/1473325017707027 https://journals.sagepub.com/doi/pdf/10.1177/1473325017707027,http://dx.doi.org/10.1177/1473325017707027,,10.1177/1473325017707027,2607691026,,0,002-204-247-906-240; 004-047-400-573-566; 008-163-922-560-58X; 018-392-325-202-807; 023-015-640-261-177; 023-065-339-932-512; 023-632-592-308-658; 025-652-316-898-728; 026-657-034-258-481; 027-613-943-784-165; 028-294-833-857-619; 031-406-241-534-244; 034-892-823-339-532; 036-163-818-173-059; 041-547-051-837-473; 046-095-328-353-330; 049-712-889-485-333; 056-431-607-745-938; 066-725-359-518-774; 074-180-899-345-491; 104-726-905-977-726; 106-887-780-492-804; 107-743-637-677-228; 108-951-698-146-95X; 122-204-081-635-831; 123-265-494-065-078; 127-214-707-382-225; 159-396-838-513-45X; 162-004-344-494-976; 180-067-422-216-287; 192-163-015-589-612; 197-317-649-488-603,8,false,,
+091-279-933-347-279,Data Publication for Personalised Health Data,2023-09-07,2023,journal article,Proceedings of the Conference on Research Data Infrastructure,2941296x,TIB Open Publishing,,Juliane Fluck; Martin Golebiewski; Johannes Darms,"<jats:p>Health data collected in clinical trials and epidemiological as well as public health studies cannot be freely published, but are valuable datasets whose subsequent use is of high importance for health research. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) aims to promote the publication of such health data without compromising privacy. Based on existing international standards, NFDI4Health has established a generic information model for the description and preservation of high-level metadata describing health-related studies, covering both clinical and epidemiological studies. As an infrastructure for publishing such preservation metadata as well as more detailed representation information of study data (e.g. questionaries and data dictionaries), NFDI4Health has developed the German Central Health Study Hub. Content is either harvested from existing distributed sources or entered directly via a user interface. This metadata makes health studies more discoverable, and researchers can use the published metadata to evaluate the content of data collections, learn about access conditions and how and where to request data access. The goal of NFDI4Health is to establish interoperable and internationally accepted standards and processes for the publication of health data sets to make health data FAIR.</jats:p>",1,,,,Metadata; Computer science; Interoperability; Health data; Data access; Data science; Data publishing; Data sharing; Public health; Publishing; World Wide Web; Medicine; Health care; Database; Political science; Alternative medicine; Nursing; Pathology; Law,,,,Deutsche Forschungsgemeinschaft; Deutsche Forschungsgemeinschaft,,http://dx.doi.org/10.52825/cordi.v1i.392,,10.52825/cordi.v1i.392,,,0,018-659-672-812-247; 028-883-542-405-239; 042-090-843-744-055; 070-462-022-251-464,0,true,,hybrid
+094-587-538-208-969,Health Data in Ontario,2009-11-24,2009,,,,,,Susan Bondy,,,,,,Geography; Health data; Environmental health,,,,,https://ir.lib.uwo.ca/rdchealthconference/preconference/program/4/,https://ir.lib.uwo.ca/rdchealthconference/preconference/program/4/,,,1492361140,,0,,0,false,,
+097-441-014-118-855,MGH Guides: Health Data Sources: Health Data Sources,2018-11-01,2018,libguide,,,,,Jessica Bell,,,,,,Geography; Health data; Environmental health,,,,,https://libguides.massgeneral.org/c.php?g=888627,https://libguides.massgeneral.org/c.php?g=888627,,,3215088546,,0,,0,false,,
+099-059-952-089-080,Health Data,2021-09-06,2021,book chapter,Introduction to Deep Learning for Healthcare,,Springer International Publishing,,Cao Xiao; Jimeng Sun,,,,9,22,Health data; Computer science; Modalities; Unstructured data; Data type; Data science; Data mining; Big data; Health care; Sociology; Political science; Social science; Law; Programming language,,,,,,http://dx.doi.org/10.1007/978-3-030-82184-5_2,,10.1007/978-3-030-82184-5_2,,,0,013-421-529-237-735; 020-503-868-606-067; 036-469-063-533-440; 061-185-313-099-360,1,false,,
+100-002-170-448-970,LibGuides: Health Data Resources: Other Sources of Health Data,2011-10-13,2011,,,,,,Douglas Joubert,"This online guide contains information on finding, using, and analyzing health and population data. The focus of this guide is on publicly available data sources and freely available analysis tools.",,,,,Public health informatics; Health indicator; Public health; HRHIS; Data science; Analysis tools; Population data; Health data; Focus (computing); Medicine,,,,,,,,,1769479078,,0,,0,false,,
+102-277-633-006-200,Geospatial Health Data,2019-11-21,2019,book,,,Chapman and Hall/CRC,,Paula Moraga,,,,,,Geospatial analysis; Data science; Computer science; Geography; Cartography,,,,,,http://dx.doi.org/10.1201/9780429341823,,10.1201/9780429341823,,,0,,73,false,,
+103-303-166-784-70X,Health Data Processing - Managing and Integrating Clinical Data: Health Records,,2018,book chapter,Health Data Processing,,Elsevier,,Marius Fieschi,,,,121,136,Health records; Medical emergency; Medicine,,,,,https://api.elsevier.com/content/article/PII:B9781785482878500092?httpAccept=text/xml,http://dx.doi.org/10.1016/b978-1-78548-287-8.50009-2,,10.1016/b978-1-78548-287-8.50009-2,2895377369,,0,,0,false,,
+105-148-472-334-104,LibGuides: Global Health Data: Global Health Data,2010-07-15,2010,libguide,,,,,Mary White; null Mshi; null Ahip,,,,,,Political science; Global health; Economic growth,,,,,https://guides.lib.unc.edu/global_health_data?hs=a,https://guides.lib.unc.edu/global_health_data?hs=a,,,2883357770,,0,,0,false,,
+106-262-645-367-904,Health Services Data: Typology of Health Care Data,,2015,book chapter,Data and Measures in Health Services Research,,Springer US,,Ross M. Mullner,Basic Units of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Individuals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Groups/Populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Health Care Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Health Care Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 National Health Care Systems . . . . . . . . . . . . . . . . . . . . . . . . 7,,,1,32,Health administration; HRHIS; Psychology; Nursing; Health care; Health services; National health; Health policy; Family medicine; Unit of analysis; Typology,,,,,https://link-springer-com-443.webvpn.jmu.edu.cn/referenceworkentry/10.1007%2F978-1-4899-7673-4_6-1 https://link-springer-com-443.webvpn.jmu.edu.cn/content/pdf/10.1007%2F978-1-4899-7673-4_6-1.pdf http://link.springer.com/content/pdf/10.1007%2F978-1-4899-7673-4_6-1.pdf,http://dx.doi.org/10.1007/978-1-4899-7673-4_6-1,,10.1007/978-1-4899-7673-4_6-1,2522729009,,0,000-366-208-221-985; 002-527-515-183-162; 005-958-452-227-386; 009-092-590-813-659; 014-140-218-278-05X; 017-594-054-344-65X; 022-789-077-524-70X; 023-282-536-920-079; 025-798-701-183-114; 032-192-698-078-790; 032-808-101-125-906; 035-900-905-291-417; 037-951-253-812-40X; 041-521-536-824-560; 061-788-469-784-033; 065-096-116-210-040; 069-465-878-508-691; 070-130-003-275-013; 074-275-980-004-055; 076-508-077-325-087; 082-467-814-322-505; 083-557-754-321-206; 094-428-072-929-28X; 105-534-002-753-668; 111-479-436-985-958; 142-660-672-050-717; 147-260-852-741-995; 154-217-925-691-088; 158-066-799-477-290; 164-979-246-230-785; 172-115-983-943-664; 173-700-033-271-60X; 181-400-116-367-549; 187-832-803-478-248; 190-727-090-169-560; 191-440-264-563-241,1,false,,
+106-588-194-599-22X,Administrative Health Data,,2005,,,,,,Lisa I. Iezzoni; Michael Shwartz; Arlene S. Ash,,,,,,Health education; Health administration; HRHIS; Health care; Health promotion; International health; Administrative services organization; Public administration; Health policy; Medicine,,,,,https://escholarship.umassmed.edu/qhs_pp/747/,https://escholarship.umassmed.edu/qhs_pp/747/,,,1538644127,,0,,3,false,,
+108-419-760-840-176,Lifeblood of Health is Data,2022-01-01,2022,journal article,IEEE MultiMedia,1070986x; 19410166,Institute of Electrical and Electronics Engineers (IEEE),United States,Ramesh Jain,"Health is the most important aspect of life. However, health is the least developed aspect of modern society that increasingly uses data as a major resource for understanding complexities in a system and relies on predictive control for the overall improvement of systems. Progress in biology, multimodal sensors, mobile devices, computing, and related areas is rapidly establishing that health is a perpetual personal data system rather than current population-based episodic disease-oriented healthcare approach. Lifestyle is more important for controlling health than medicine. Technology now allows us to perpetually measure lifestyle and analyze it to build personal models and estimate health state for developing predictive, preventive, personalized approach for maximizing healthiness for most people in the world. These emerging approaches have demonstrated their early success and are now ready for helping people achieve their health goals. Data is the lifeblood of health; technology has a great opportunity to bring this imminent transformation now.",29,1,128,135,Computer science; Health care; Data science; Big data; Resource (disambiguation); eHealth; Population; Internet privacy; Risk analysis (engineering); Business; Medicine; Environmental health; Political science; Data mining; Computer network; Law,,,,,,http://dx.doi.org/10.1109/mmul.2022.3151996,,10.1109/mmul.2022.3151996,,,0,003-990-168-219-167; 021-438-622-948-313; 025-342-683-343-413; 031-912-653-678-502; 056-678-779-321-108; 056-904-525-618-641; 070-593-252-619-033; 085-065-195-711-022; 175-828-196-886-719,1,false,,
+110-488-993-567-984,Secondary Data Sources for Public Health: Health Services Utilization Data,2007-04-09,2007,book chapter,Secondary Data Sources for Public Health,,Cambridge University Press,,Sarah Boslaugh,,,,12,33,Epidemiology; Public health; HRHIS; Health services; Probability sampling; Data access; Family medicine; Medicine; Secondary data,,,,,https://www.cambridge.org/core/books/secondary-data-sources-for-public-health/health-services-utilization-data/BE2460213D6FF6DF85A0287AF74EFA1F,http://dx.doi.org/10.1017/cbo9780511618802.003,,10.1017/cbo9780511618802.003,1508676237,,0,,0,false,,
+113-524-576-929-029,Health Data,2020-12-22,2020,book chapter,Child Data Citizen,,The MIT Press,,,,,,51,70,Computer science,,,,,,http://dx.doi.org/10.7551/mitpress/12415.003.0005,,10.7551/mitpress/12415.003.0005,,,0,,0,false,,
+117-376-907-035-753,Guides: Health Data: Cancer Data,2018-10-26,2018,libguide,,,,,Ken Carriveau,,,,,,Health data; Cancer data; Family medicine; Medicine,,,,,https://researchguides.baylor.edu/c.php?g=886952&p=6374234,https://researchguides.baylor.edu/c.php?g=886952&p=6374234,,,3190833508,,0,,0,false,,
+119-762-466-912-156,MGH Guides: Health Data Sources: Data for Population Health,2018-11-01,2018,,,,,,Amanda Tarbet,,,,,,Geography; Population health; Health data; Environmental health,,,,,,,,,2950343282,,0,,0,false,,
+120-516-312-595-524,Cooperation between the Health Data Hub and the Health Data Lab at the BfArM in setting up the European Health Data Space,2024-01-10,2024,journal article,"Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz",14371588; 14369990,Springer Science and Business Media LLC,Germany,Roxana Arzideh; Mario Jendrossek; Irene Zittlau,"In recent years, the cross-border exchange between European member states regarding the use of health data has become increasingly relevant. In order to jointly counteract the current heterogeneity and health-related challenges as well as to successively implement the vision of the European Health Data Space (EHDS) in compliance with high data protection standards, different projects with the participation of the Health Data Lab at the Federal Institute for Drugs and Medical Devices (BfArM) and the Health Data Hub have been initiated. This close German-French cooperation on the one hand creates synergies and examines challenges and possible solutions on a European level. On the other hand, the cooperation between both institutions promotes the cross-border exchange of knowledge and experience in order to jointly meet challenges and achieve goals at national and European levels.",,,,,Political science; Humanities; Art,Digitalization; EHDS; Health Data; Interoperability; Secondary health data,,,Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM),,http://dx.doi.org/10.1007/s00103-023-03829-7,38197926,10.1007/s00103-023-03829-7,,,0,,0,true,,hybrid
+125-517-779-120-713,LibGuides: Public Health: Health Data,2011-04-28,2011,libguide,,,,,Dana Abbey,,,,,,Public health; Health data; Medicine; Environmental health,,,,,https://library-cuanschutz.libguides.com/c.php?g=259527&p=1732594 https://hslibraryguides.ucdenver.edu/c.php?g=259527&p=1732594 http://hslibraryguides.ucdenver.edu/c.php?g=259527&p=1732594,https://library-cuanschutz.libguides.com/c.php?g=259527&p=1732594,,,1466972593,,0,,0,false,,
+125-973-778-009-190,Health Data,,2022,reference entry,Oxford Encyclopedia of EU Law,,Oxford University Press,,Shabani Mahsa,,,,,,Computer science,,,,,,http://dx.doi.org/10.1093/law-oeeul/e181.013.181,,10.1093/law-oeeul/e181.013.181,,,0,,2,false,,
+126-316-025-824-445,Wiki-Health: A Big Data Platform for Health Sensor Data Management,,2014,book chapter,Cloud Computing Applications for Quality Health Care Delivery,23281243; 2328126x,IGI Global,,Yang Li; Chao Wu; Li Guo; Chun-Hsiang Lee; Yike Guo,"<jats:p>Quickly evolving modern technologies such as cloud computing, Internet of things, and intelligent data analysis have created great opportunities for better living. The authors visualize the role these technological innovations will play in the healthcare sector as they spearhead a shift in focus from offering better healthcare services only to people with problems to helping everyone achieve a healthier lifestyle. In this chapter, the authors first discuss the existing and potential barriers followed by an in-depth demonstration of a service platform named Wiki-Health that takes advantage of cloud computing and Internet of things for personal well-being data management. It is a social platform, which is designed and implemented for data-driven and context-specific discovery of citizen communities in the areas of health, fitness, and well-being. At the end of the chapter, the authors analyse a case study to illustrate how the Wiki-Health platform can be used to serve a real world personal health training application. </jats:p>",,,59,77,Software engineering; Engineering; Software system; Data management; Component-based software engineering; Social software engineering; Big data,,,,,https://www.igi-global.com/chapter/wiki-health/110429,http://dx.doi.org/10.4018/978-1-4666-6118-9.ch004,,10.4018/978-1-4666-6118-9.ch004,2503854658,,0,000-277-406-062-60X; 004-405-561-772-31X; 006-690-084-358-113; 014-322-616-966-293; 016-724-657-124-679; 019-068-029-335-682; 022-945-260-865-969; 030-636-979-324-70X; 031-612-703-045-160; 031-896-755-578-767; 032-452-538-246-298; 033-333-734-978-28X; 037-594-002-910-352; 041-151-716-660-940; 042-404-320-990-766; 049-974-628-555-225; 053-290-503-160-032; 055-198-723-049-919; 061-923-382-435-358; 062-805-543-471-018; 074-885-257-614-231; 089-151-349-873-398; 091-080-142-023-528; 092-941-553-775-191; 104-381-562-111-806; 105-955-193-288-265; 109-533-946-783-419; 113-062-644-833-229; 137-531-493-021-986; 149-499-362-466-158; 168-020-617-512-497; 171-506-879-823-664; 184-038-718-938-628,13,false,,
+128-409-662-526-868,Mental health data.,1982-04-01,1982,journal article,PubMed,,,,,,34,3,21,2,Mental health; MEDLINE; Medicine; Psychiatry; Political science; Law,,,,,,,,,,,0,,0,false,,
+131-812-311-826-314,Secondary Data Sources for Public Health: Data on Multiple Health Topics,2007-04-09,2007,book chapter,Secondary Data Sources for Public Health,,Cambridge University Press,,Sarah Boslaugh,,,,47,64,Epidemiology; Public health; Multiple cause of death; Nhanes i; Data access; Family medicine; Medicine; Secondary data; Environmental health,,,,,http://ebooks.cambridge.org/chapter.jsf?bid=CBO9780511618802&cid=CBO9780511618802A026,http://dx.doi.org/10.1017/cbo9780511618802.005,,10.1017/cbo9780511618802.005,647359066,,0,,0,false,,
+136-664-026-814-517,Health data privacy.,,1997,journal article,Physician executive,08982759,American College of Physician Executives,United States,Benjamin Gc; Kennan Sa,"How can the tradition of patient-provider confidentiality be preserved and still meet the changing information needs of insurers, employers, public health agencies, policy analysts, and researchers? As countries grapple with the issue of a patient's right to medical record privacy, actions that the U.S. takes in this area will set precedents and have implications for nearly every health care electronic transaction. The Health Insurance Portability and Accountability Act requires the Secretary of Health and Human Services (HHS) to recommend standards for electronic financial and administrative transactions. The HHS focused on the confidentially of patient's health care information, guided by five principles in developing recommendations: (1) Boundaries; (2) Security; (3) Consumer Control; (4) Accountability; and (5) Public Responsibility.",23,8,64,66,Internet privacy; Information privacy; Public health; Business; Health care; Medical record; Accountability; Health Insurance Portability and Accountability Act; Masking (Electronic Health Record); Confidentiality,,Computer Security/legislation & jurisprudence; Confidentiality/legislation & jurisprudence; Consumer Advocacy; Data Collection; Insurance Claim Review/legislation & jurisprudence; Medical Records/legislation & jurisprudence; Privacy/legislation & jurisprudence; Social Responsibility; United States; United States Dept. of Health and Human Services,,,https://www.ncbi.nlm.nih.gov/pubmed/10176693,https://www.ncbi.nlm.nih.gov/pubmed/10176693,10176693,,2468504085,,0,,1,false,,
+137-684-518-305-027,Health Data Analytics,2019-01-15,2019,book chapter,Big Data in eHealthcare,,Chapman and Hall/CRC,,Nandini Mukherjee; Sarmistha Neogy; Samiran Chattopadhyay,,,,87,128,Data science; Health data; Computer science; Analytics,,,,,https://content.taylorfrancis.com/books/download?dac=C2017-0-63973-1&isbn=9781351057790&doi=10.1201/9781351057790-5&format=pdf https://www.taylorfrancis.com/chapters/health-data-analytics-nandini-mukherjee-sarmistha-neogy-samiran-chattopadhyay/10.1201/9781351057790-5,http://dx.doi.org/10.1201/9781351057790-5,,10.1201/9781351057790-5,2914216690,,0,133-339-281-154-925,0,false,,
+138-372-589-265-038,Health and Public Health Data,2016-04-26,2016,,,,,,Virginia Dato; Susan Salkowitz,,,,,,Public health; Business; Environmental health,,,,,https://osf.io/95fzx/,https://osf.io/95fzx/,,,2902491699,,0,,0,false,,
+141-730-467-131-731,Administrative Health Data,2005-07-14,2005,book chapter,Health Statistics,,Oxford University Press,,Lisa I. Iezzoni; Michael Shwartz; Arlene S. Ash,"Administrative data provide important information about health services use, expenditures, selected clinical outcomes, and quality of care. This chapter examines U.S. administrative databases. It briefly discusses data systems internationally and explores their utility for population-based studies of health and health care.",,,139,160,Health care; Quality (philosophy); Data quality; Population health; Health services; Business; Health data; Population; Medicine; Environmental health; Economic growth; Economics; Marketing; Metric (unit); Philosophy; Epistemology,,,,,,http://dx.doi.org/10.1093/acprof:oso/9780195149289.003.0006,,10.1093/acprof:oso/9780195149289.003.0006,,,0,,2,false,,
+144-880-400-018-075,Big Data for Health,2018-03-05,2018,book chapter,Encyclopedia of Big Data Technologies,,Springer International Publishing,,Valerio Persico,,,,1,10,Environmental science; Geography,,,,,,http://dx.doi.org/10.1007/978-3-319-63962-8_25-1,,10.1007/978-3-319-63962-8_25-1,,,0,006-910-747-234-851; 007-845-113-881-254; 008-127-396-671-053; 008-576-508-265-900; 008-936-401-996-515; 010-211-878-012-054; 011-871-848-229-551; 012-918-264-608-066; 015-121-671-363-175; 015-447-374-583-278; 018-151-764-103-053; 018-313-377-143-513; 018-555-232-639-771; 019-074-104-738-862; 020-492-104-101-586; 020-717-325-036-612; 022-086-840-985-614; 022-718-915-112-176; 024-821-888-376-907; 026-546-250-204-670; 042-461-015-490-766; 044-380-508-713-727; 047-274-134-476-359; 049-446-095-340-766; 051-592-555-054-697; 051-742-983-377-856; 053-445-152-187-56X; 058-723-197-720-830; 060-871-414-825-628; 065-085-112-949-493; 067-030-064-944-093; 081-320-315-470-228; 089-610-793-498-790; 090-011-098-612-177; 090-846-798-251-902; 091-952-129-951-085; 106-414-878-189-061; 106-640-718-934-961; 127-873-622-875-548; 135-863-967-339-573; 160-217-643-168-923; 179-617-707-926-045; 188-332-136-393-173,1,false,,
+148-716-897-042-302,LibGuides: Global Health Data: Global Health Data,2010-07-15,2010,libguide,,,,,Kate McGraw; null Ma; null Mls,,,,,,Political science; Global health; Economic growth,,,,,https://guides.lib.unc.edu/c.php?g=8398&p=43275,https://guides.lib.unc.edu/c.php?g=8398&p=43275,,,1658322838,,0,,0,false,,
+149-256-312-121-518,LibGuides: Global Health Data: Global Health Data,2010-07-15,2010,libguide,,,,,Mls Sarah Towner Wright,,,,,,Political science; Global health; Economic growth,,,,,https://guides.lib.unc.edu/global_health_data,https://guides.lib.unc.edu/global_health_data,,,3184382527,,0,,0,false,,
+150-108-865-825-305,Health data.,1968-08-01,1968,journal article,PubMed,,,,,,,,,,Computer science,,,,,,,,,,,0,,0,false,,
+150-447-174-431-733,Health Data in Germany,2006-03-01,2006,journal article,Journal of Contextual Economics – Schmollers Jahrbuch,2568762x; 25687603,Duncker & Humblot GmbH,,Karin Böhm,,126,3,473,481,Psychology,,,,,,http://dx.doi.org/10.3790/schm.126.3.473,,10.3790/schm.126.3.473,,,0,,0,false,,
+152-118-678-572-674,Research Guides: Statistics & Data Sets for Health Care and Public Health: Global Health Data,2016-06-22,2016,libguide,,,,,Elaina Vitale,,,,,,Public health; Nursing; Health care; Political science; Global health,,,,,https://researchguides.dartmouth.edu/c.php?g=517073&p=6289100,https://researchguides.dartmouth.edu/c.php?g=517073&p=6289100,,,2964736752,,0,,0,false,,
+158-525-542-036-393,Digitalization of Health Data: Interoperability of the Proposed European Health Data Space.,2022-08-31,2022,journal article,Studies in health technology and informatics,18798365; 09269630,IOS Press,Netherlands,Caroline Stellmach; Michael R Muzoora; Sylvia Thun,"On May 3rd, 2022, the European Commission published its legislative proposal to create a European Health Data Space (EHDS) enabling citizens of the European Union to gain secure access to their electronic health data by establishing a market for digital health. This market will feature the primary and secondary use of electronic health records by digital products and services. The articles of the proposal address many aspects of ensuring health data interoperability. That includes the creation of a European Electronic Health Record Exchange Format for defined data categories including patient summaries and electronic prescriptions, the development of a central platform to provide a cross-border digital infrastructure and that each Member State institutes a digital health authority and a national point of contact. In addition, the Commission will define common specifications that electronic health record systems and medical devices will have to meet as interoperability requirements. In its current form, the proposal does not stipulate specific standards that need to be universally adopted to ensure semantic and syntactical interoperability. Considering that many datasets are not internationally harmonized and lack standardization, these specifications will need to be provided for example by existing standards like the International Patient Summary.",298,,132,,Interoperability; Standardization; Semantic interoperability; European union; Digital health; Cross-domain interoperability; Health data; Legislature; Computer science; Business; Computer security; World Wide Web; Health care; Political science; Law; Economic policy; Operating system,European Health Data Space; digital health data; electronic health records; interoperability,Electronic Health Records; European Union; Humans,,,,http://dx.doi.org/10.3233/shti220922,36073471,10.3233/shti220922,,,0,,3,true,,hybrid
+159-089-407-353-562,LibGuides: Global Health Data: Global Health Data,2010-07-15,2010,libguide,,,,,Megan Fratta,,,,,,Political science; Global health; Economic growth,,,,,https://guides.lib.unc.edu/global_health_data,https://guides.lib.unc.edu/global_health_data,,,2796802166,,0,,0,false,,
+163-003-110-038-945,Mental health data.,1987-07-09,1987,journal article,PubMed,,,,,,39,6,22,4,Mental health; MEDLINE; Medicine; Psychiatry; Psychology; Political science; Law,,,,,,,,,,,0,,0,false,,
+167-837-318-644-107,LibGuides: Data: Health Data,2020-07-10,2020,libguide,,,,,Julia Bauder,,,,,,Geography; Health data; Environmental health,,,,,https://grinnell.libguides.com/c.php?g=1057667&p=7685587,https://grinnell.libguides.com/c.php?g=1057667&p=7685587,,,3165426697,,0,,0,false,,
+168-291-924-616-952,Health Data Governance,2015-10-05,2015,book,OECD Health Policy Studies,2074319x; 20743181,OECD,,,,,,,,Corporate governance; Political science; Business; Finance,,,,,,http://dx.doi.org/10.1787/9789264244566-en,,10.1787/9789264244566-en,,,0,,19,false,,
+168-678-526-357-744,Guides: Health Data: Mental Health,2018-10-26,2018,libguide,,,,,Ken Carriveau,,,,,,Psychiatry; Mental health; Psychology; Health data,,,,,https://researchguides.baylor.edu/c.php?g=886952&p=6374258,https://researchguides.baylor.edu/c.php?g=886952&p=6374258,,,3192930960,,0,,0,false,,
+174-710-565-070-855,OECD Health Data: Health status,2017-11-15,2017,dataset,OECD Health Statistics,,OECD,,,,,,,,Health data; Environmental health; Data science; Computer science; Medicine; Political science; Health care; Law,,,,,,http://dx.doi.org/10.1787/data-00540-en,,10.1787/data-00540-en,,,0,,3,false,,
+183-932-037-209-673,Guides: Health Data: State Health,2018-10-26,2018,libguide,,,,,Ken Carriveau,,,,,,Political science; Health data; Public administration; State (computer science),,,,,https://researchguides.baylor.edu/c.php?g=886952&p=6374274,https://researchguides.baylor.edu/c.php?g=886952&p=6374274,,,3188039681,,0,,0,false,,
+187-708-678-940-22X,Research: Health Communication: Health Data & Data Visualization Tools,2017-11-27,2017,,,,,,BU Libraries,,,,,,Data visualization; Data science; Health communication; Health data; Computer science,,,,,https://library.bu.edu/healthcom/data,https://library.bu.edu/healthcom/data,,,3162327538,,0,,0,false,,
+188-149-795-093-70X,Guides. GIS & Health . Health Data.,2012-02-23,2012,,,,,,Arwen Meador,,,,,,GIS and public health; Geography; Health data; Environmental health,,,,,,,,,448187795,,0,,0,false,,
+189-095-088-253-319,Health data.,,1968,journal article,California medicine,00081264,California Medical Association,United States,,,109,2,174,,,,,,,,https://www.ncbi.nlm.nih.gov/pubmed/18730144,18730144,,,PMC1503179,0,,0,true,,unknown
+193-541-653-240-37X,Towards Quality Health Data: Defining the Health Data Pyramid,2017-04-24,2017,,,,,,Jessica E. Lockery,,,,,,Quality (business); Health data; Computer science; Knowledge management; Pyramid,,,,,https://research.monash.edu/en/publications/towards-quality-health-data-defining-the-health-data-pyramid,https://research.monash.edu/en/publications/towards-quality-health-data-defining-the-health-data-pyramid,,,2972796940,,0,,0,false,,
+195-355-386-073-91X,Big Data for Health,2019-02-20,2019,book chapter,Encyclopedia of Big Data Technologies,,Springer International Publishing,,Valerio Persico,,,,244,254,Feeling; Big data; Health care; Raising (metalworking); State (computer science); Business; Data science; Psychology; Computer science; Political science; Engineering; Social psychology; Data mining; Mechanical engineering; Algorithm; Law,,,,,,http://dx.doi.org/10.1007/978-3-319-77525-8_25,,10.1007/978-3-319-77525-8_25,,,0,006-910-747-234-851; 007-845-113-881-254; 008-127-396-671-053; 008-576-508-265-900; 008-936-401-996-515; 010-211-878-012-054; 011-871-848-229-551; 012-918-264-608-066; 015-121-671-363-175; 015-447-374-583-278; 018-151-764-103-053; 018-313-377-143-513; 018-555-232-639-771; 019-074-104-738-862; 020-492-104-101-586; 020-717-325-036-612; 022-086-840-985-614; 022-718-915-112-176; 024-821-888-376-907; 026-546-250-204-670; 042-461-015-490-766; 044-380-508-713-727; 047-274-134-476-359; 049-446-095-340-766; 051-592-555-054-697; 053-445-152-187-56X; 055-389-173-982-684; 058-723-197-720-830; 060-871-414-825-628; 065-085-112-949-493; 067-030-064-944-093; 081-320-315-470-228; 089-610-793-498-790; 090-011-098-612-177; 090-846-798-251-902; 091-952-129-951-085; 106-414-878-189-061; 106-640-718-934-961; 127-873-622-875-548; 135-863-967-339-573; 160-217-643-168-923; 188-332-136-393-173,0,false,,
+197-494-743-842-688,LibGuides: Finding Data: Health Data,2017-03-10,2017,libguide,,,,,Hedda Monaghan,,,,,,Sociology; Chemistry (relationship); Global studies; Health data; Engineering ethics,,,,,https://libguides.framingham.edu/c.php?g=640413&p=4488586,https://libguides.framingham.edu/c.php?g=640413&p=4488586,,,3092353174,,0,,0,false,,

test_function.py

@@ -0,0 +1,50 @@
+import scattertext as st
+import spacy
+import pandas as pd
+import en_core_web_md
+
+
+
+# load language model:
+nlp = en_core_web_md.load()
+nlp = spacy.load("en_core_web_md")
+
+def customized_file(file_path: str,column_category: str, column_text: str, subcategory1: str, subcategory2: str):
+    '''
+    generate plot from user selected file
+    :param file_path: the path of file to be analysis. it should be related path as the file or file directory should be in the same father directory of the script
+    :param column_category: header of the subcategories
+    :param column_text: header of text to be plotted
+    :param subcategory1: the subcategory displayed on X-axis
+    :param subcategory2: the subcategory displayed on Y-axis
+    :return: the HTML path of scattertext plot
+    '''
+    # proceed data
+    if file_path.endswith(".csv"):
+        df = pd.read_csv(file_path)
+    elif file_path.endswith(".txt"):
+        df = pd.read_table(file_path, sep='\t')  # Doc: assume contents are seperated by Tabs.
+    else:
+        raise ValueError("Unsupported file format.")
+
+    # filter Dataframe with target subcategories
+    df_filtered = df[df[column_category].isin([subcategory1, subcategory2])]
+    # convert to scattertext corpus
+    corpus = st.CorpusFromPandas(df_filtered, category_col=column_category, text_col=column_text, nlp=nlp).build()
+    # create visualization
+    html = st.produce_scattertext_explorer(corpus,
+                                            category = subcategory1,
+                                            category_name = subcategory1,
+                                            not_category_name = subcategory2,
+                                            width_in_pixels = 1000,
+                                            minimum_term_frequency = 0,
+                                            metadata = df_filtered['column_category'])
+
+    html_file_path = "scattertext_visualization.html"
+    with open(html_file_path, "w", encoding='utf-8') as f:
+        f.write(html)
+
+    return html_file_path
+
+path = customized_file('./sample_data/lens.csv', 'Lens ID','Abstract', '004-110-551-439-89X', '041-088-173-402-38X' )
+