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Li X, Sun K, Chen Y, Yuan Y. Study on the Gas-Chromic Character of Pd/TiO 2 for Fast Room-Temperature CO Detection. Molecules 2024; 29:3843. [PMID: 39202922 PMCID: PMC11357185 DOI: 10.3390/molecules29163843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Revised: 08/06/2024] [Accepted: 08/07/2024] [Indexed: 09/03/2024] Open
Abstract
As a widely used support, TiO2 has often been combined with Pd to form highly sensitive gas-chromic materials. Herein, we prepared a series of Pd/TiO2 catalysts with different Pd content (from 0.1 to 5 wt.%) by the impregnation method for their utilization in fast room-temperature CO detection. The detection was simply based on visible color change when the Pd/TiO2 was exposed to CO. The sample with 1 wt.% Pd/TiO2 presented an excellent CO gasochromic character, associated with a maximum chromatic aberration value of 90 before and after CO exposure. Systematic catalyst characterizations of XPS, FT-IR, CO-TPD, and N2 adsorption-desorption and density functional theory calculations for the CO adsorption and charge transfer over the Pd and PdO surfaces were further carried out. It was found that the interaction between CO and the Pd surface was strong, associated with a large adsorption energy of -1.99 eV and charge transfer of 0.196 e. The color change was caused by a reduction in Pd2+ to metallic Pd0 over the Pd/TiO2 surface after CO exposure.
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Affiliation(s)
- Xinbao Li
- College of Energy Environment and Safety Engineering, China Jiliang University, Hangzhou 310018, China
| | - Kai Sun
- Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
| | - Ying Chen
- Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
| | - Ye Yuan
- Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
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2
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Guo Z, Wu C. Low Temperature CO Oxidation over Co3O4 Monolithic Catalysts on a Series of Metal Foams. KINETICS AND CATALYSIS 2022. [DOI: 10.1134/s002315842108005x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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3
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Sung SF, Hsieh CY, Hu YH. Two Decades of Research Using Taiwan's National Health Insurance Claims Data: Bibliometric and Text Mining Analysis on PubMed. J Med Internet Res 2020; 22:e18457. [PMID: 32543443 PMCID: PMC7327589 DOI: 10.2196/18457] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/12/2020] [Accepted: 04/16/2020] [Indexed: 12/18/2022] Open
Abstract
Background Studies using Taiwan’s National Health Insurance (NHI) claims data have expanded rapidly both in quantity and quality during the first decade following the first study published in 2000. However, some of these studies were criticized for being merely data-dredging studies rather than hypothesis-driven. In addition, the use of claims data without the explicit authorization from individual patients has incurred litigation. Objective This study aimed to investigate whether the research output during the second decade after the release of the NHI claims database continues growing, to explore how the emergence of open access mega journals (OAMJs) and lawsuit against the use of this database affect the research topics and publication volume and to discuss the underlying reasons. Methods PubMed was used to locate publications based on NHI claims data between 1996 and 2017. Concept extraction using MetaMap was employed to mine research topics from article titles. Research trends were analyzed from various aspects, including publication amount, journals, research topics and types, and cooperation between authors. Results A total of 4473 articles were identified. A rapid growth in publications was witnessed from 2000 to 2015, followed by a plateau. Diabetes, stroke, and dementia were the top 3 most popular research topics whereas statin therapy, metformin, and Chinese herbal medicine were the most investigated interventions. Approximately one-third of the articles were published in open access journals. Studies with two or more medical conditions, but without any intervention, were the most common study type. Studies of this type tended to be contributed by prolific authors and published in OAMJs. Conclusions The growth in publication volume during the second decade after the release of the NHI claims database was different from that during the first decade. OAMJs appeared to provide fertile soil for the rapid growth of research based on NHI claims data, in particular for those studies with two or medical conditions in the article title. A halt in the growth of publication volume was observed after the use of NHI claims data for research purposes had been restricted in response to legal controversy. More efforts are needed to improve the impact of knowledge gained from NHI claims data on medical decisions and policy making.
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Affiliation(s)
- Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan.,Department of Information Management, Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan
| | - Cheng-Yang Hsieh
- Department of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan.,School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ya-Han Hu
- Department of Information Management, National Central University, Taoyuan City, Taiwan.,Center for Innovative Research on Aging Society, National Chung Cheng University, Chiayi County, Taiwan.,MOST AI Biomedical Research Center, National Cheng Kung University, Tainan, Taiwan
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4
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Juarez PD, Tabatabai M, Burciaga Valdez R, Hood DB, Im W, Mouton C, Colen C, Al-Hamdan MZ, Matthews-Juarez P, Lichtveld MY, Sarpong D, Ramesh A, Langston MA, Rogers GL, Phillips CA, Reichard JF, Donneyong MM, Blot W. The Effects of Social, Personal, and Behavioral Risk Factors and PM 2.5 on Cardio-Metabolic Disparities in a Cohort of Community Health Center Patients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E3561. [PMID: 32438697 PMCID: PMC7277630 DOI: 10.3390/ijerph17103561] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/30/2020] [Accepted: 05/15/2020] [Indexed: 12/26/2022]
Abstract
(1) Background: Cardio-metabolic diseases (CMD), including cardiovascular disease, stroke, and diabetes, have numerous common individual and environmental risk factors. Yet, few studies to date have considered how these multiple risk factors together affect CMD disparities between Blacks and Whites. (2) Methods: We linked daily fine particulate matter (PM2.5) measures with survey responses of participants in the Southern Community Cohort Study (SCCS). Generalized linear mixed modeling (GLMM) was used to estimate the relationship between CMD risk and social-demographic characteristics, behavioral and personal risk factors, and exposure levels of PM2.5. (3) Results: The study resulted in four key findings: (1) PM2.5 concentration level was significantly associated with reported CMD, with risk rising by 2.6% for each µg/m3 increase in PM2.5; (2) race did not predict CMD risk when clinical, lifestyle, and environmental risk factors were accounted for; (3) a significant variation of CMD risk was found among participants across states; and (4) multiple personal, clinical, and social-demographic and environmental risk factors played a role in predicting CMD occurrence. (4) Conclusions: Disparities in CMD risk among low social status populations reflect the complex interactions of exposures and cumulative risks for CMD contributed by different personal and environmental factors from natural, built, and social environments.
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Affiliation(s)
- Paul D. Juarez
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA; (W.I.); (P.M.-J.)
| | - Mohammad Tabatabai
- School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA;
| | - Robert Burciaga Valdez
- RWJF Professor, Department of Family & Community Medicine AND Economics, University of New Mexico, Albuquerque, NM 87131, USA;
| | - Darryl B. Hood
- Department of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, OH 43210, USA;
| | - Wansoo Im
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA; (W.I.); (P.M.-J.)
| | - Charles Mouton
- Department of Family Medicine, University of Texas Medical Branch, Galveston, TX 77555, USA;
| | - Cynthia Colen
- Department of Sociology, Ohio State University, Columbus, OH 43210, USA;
| | - Mohammad Z. Al-Hamdan
- Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL 35805, USA;
| | - Patricia Matthews-Juarez
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA; (W.I.); (P.M.-J.)
| | - Maureen Y. Lichtveld
- Department of Environmental Health Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA;
| | - Daniel Sarpong
- Department of Biostatistics, Xavier University, Cincinnati, OH 45207, USA;
| | - Aramandla Ramesh
- Department of Biochemistry, Cancer Biology, Neuroscience & Pharmacology, Meharry Medical College, Nashville, TN 37208, USA;
| | - Michael A. Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA; (M.A.L.); (C.A.P.)
| | - Gary L. Rogers
- National Institute for Computational Sciences, University of Tennessee, Knoxville, TN 37996, USA;
| | - Charles A. Phillips
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA; (M.A.L.); (C.A.P.)
| | - John F. Reichard
- Department of Environmental Health, Risk Science Center, University of Cincinnati, Cincinnati, OH 45221, USA;
| | - Macarius M. Donneyong
- Division of Outcomes and Translational Sciences, College of Pharmacy, Ohio State University, Columbus, OH 43210, USA;
| | - William Blot
- Center for Population-based Research, Vanderbilt University, Nashville, TN 37235, USA;
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Hsieh WT, Chien TW, Kuo SC, Lin HJ. Whether productive authors using the national health insurance database also achieve higher individual research metrics: A bibliometric study. Medicine (Baltimore) 2020; 99:e18631. [PMID: 31914046 PMCID: PMC6959956 DOI: 10.1097/md.0000000000018631] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 11/21/2019] [Accepted: 12/04/2019] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Many researchers use the National Health Insurance Research Database (HIRD) to publish medical papers and gain exceptional outputs in academics. Whether they also obtain excellent citation metrics remains unclear. METHODS We searched the PubMed database (http://www.ncbi.nlm.nih.gov/pubmed) using the terms Taiwan and HIRD. We then downloaded 1997 articles published from 2012 to 2016. An authorship-weighted scheme (AWS) was applied to compute coauthor partial contributions from the article bylines. Both modified x-index and author impact factor (AIF) proved complementary to Hirsch's h-index for calculating individual research achievements (IRA). The metrics from 4684 authors were collected for comparison. Three hundred eligible authors with higher x-indexes were located and displayed on Google Maps dashboards. Ten separate clusters were identified using social network analysis (SNA) to highlight the research teams. The bootstrapping method was used to examine the differences in metrics among author clusters. The Kano model was applied to classify author IRAs into 3 parts. RESULTS The most productive author was Investigator#1 (Taichung City, Taiwan), who published 149 articles in 2015 and included 803 other members in his research teams. The Kano diagram results did not support his citation metrics beyond other clusters and individuals in IRAs. CONCLUSION The AWS-based bibliometric metrics make individual weighted research evaluations possible and available for comparison. The study results of productive authors using HIRD did not support the view that higher citation metrics exist in specific disciplines.
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Affiliation(s)
| | | | - Shu-Chun Kuo
- Department of Optometry, Chung Hwa University of Medical Technology
- Department of Ophthalmology, Chi-Mei Medical Center
| | - Hung-Jung Lin
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
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Machine Learning-Based Forecast of Hemorrhagic Stroke Healthcare Service Demand considering Air Pollution. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:7463242. [PMID: 31781360 PMCID: PMC6875383 DOI: 10.1155/2019/7463242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 09/13/2019] [Accepted: 09/25/2019] [Indexed: 02/05/2023]
Abstract
This study aimed to forecast the pattern of the demand for hemorrhagic stroke healthcare services based on air quality and machine learning. Hemorrhagic stroke, air quality, and meteorological data for 2016-2017 were obtained from the Longquanyi District of China, and the study included 1932 cases. Six machine learning methods were used to forecast the demand for hemorrhagic stroke healthcare services considering seasonality and a lag effect, and the average area under the curve was as high as 0.7971. Our results indicate that (1) the performance of forecasting during the warm season is significantly better than that in the cold season, (2) considering air pollution would improve the performance of forecasting the demand for hemorrhagic stroke healthcare services using machine learning, (3) the association between the demand for hemorrhagic stroke healthcare services and air pollutants is linear to some extent, and (4) it is feasible to use short-term concentrations of air pollutants to forecast the demand for hemorrhagic stroke healthcare services. This practical forecast model could provide an advance warning regarding the potentially high numbers of hemorrhagic stroke admissions to medical institutions, thus allowing time to implement an appropriate response to the increase in patient volumes.
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Hsieh CY, Su CC, Shao SC, Sung SF, Lin SJ, Kao Yang YH, Lai ECC. Taiwan's National Health Insurance Research Database: past and future. Clin Epidemiol 2019. [PMID: 31118821 DOI: 10.2147/clep.s196293.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Taiwan's National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan's Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.
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Affiliation(s)
- Cheng-Yang Hsieh
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan
| | - Chien-Chou Su
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Shih-Chieh Shao
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Pharmacy, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan.,Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan
| | - Swu-Jane Lin
- Department of Pharmacy Systems, Outcomes & Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA
| | - Yea-Huei Kao Yang
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Edward Chia-Cheng Lai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan
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Hsieh CY, Su CC, Shao SC, Sung SF, Lin SJ, Kao Yang YH, Lai ECC. Taiwan's National Health Insurance Research Database: past and future. Clin Epidemiol 2019; 11:349-358. [PMID: 31118821 PMCID: PMC6509937 DOI: 10.2147/clep.s196293] [Citation(s) in RCA: 729] [Impact Index Per Article: 145.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 03/12/2019] [Indexed: 01/29/2023] Open
Abstract
Taiwan’s National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan’s Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.
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Affiliation(s)
- Cheng-Yang Hsieh
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan
| | - Chien-Chou Su
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Shih-Chieh Shao
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Pharmacy, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan.,Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan
| | - Swu-Jane Lin
- Department of Pharmacy Systems, Outcomes & Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA
| | - Yea-Huei Kao Yang
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Edward Chia-Cheng Lai
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan
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Chien TW, Chang Y, Wang HY. Understanding the productive author who published papers in medicine using National Health Insurance Database: A systematic review and meta-analysis. Medicine (Baltimore) 2018; 97:e9967. [PMID: 29465594 PMCID: PMC5841958 DOI: 10.1097/md.0000000000009967] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 01/30/2018] [Accepted: 01/31/2018] [Indexed: 01/05/2023] Open
Abstract
Many researchers used National Health Insurance database to publish medical papers which are often retrospective, population-based, and cohort studies. However, the author's research domain and academic characteristics are still unclear.By searching the PubMed database (Pubmed.com), we used the keyword of [Taiwan] and [National Health Insurance Research Database], then downloaded 2913 articles published from 1995 to 2017. Social network analysis (SNA), Gini coefficient, and Google Maps were applied to gather these data for visualizing: the most productive author; the pattern of coauthor collaboration teams; and the author's research domain denoted by abstract keywords and Pubmed MESH (medical subject heading) terms.Utilizing the 2913 papers from Taiwan's National Health Insurance database, we chose the top 10 research teams shown on Google Maps and analyzed one author (Dr. Kao) who published 149 papers in the database in 2015. In the past 15 years, we found Dr. Kao had 2987 connections with other coauthors from 13 research teams. The cooccurrence abstract keywords with the highest frequency are cohort study and National Health Insurance Research Database. The most coexistent MESH terms are tomography, X-ray computed, and positron-emission tomography. The strength of the author research distinct domain is very low (Gini < 0.40).SNA incorporated with Google Maps and Gini coefficient provides insight into the relationships between entities. The results obtained in this study can be applied for a comprehensive understanding of other productive authors in the field of academics.
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Affiliation(s)
- Tsair-Wei Chien
- Medical Research Department, Chi-Mei Medical Center
- Department of Sport Management, College of Leisure and Recreation Management, Chia Nan University of Pharmacy and Science
| | - Yu Chang
- National Taiwan University School of Medicine
| | - Hsien-Yi Wang
- Department of Sport Management, College of Leisure and Recreation Management, Chia Nan University of Pharmacy and Science
- Nephrology Department, Chi-Mei Medical Center, Tainan, Taiwan
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10
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Kao CH. Clinical studies stemming from the Taiwan National Health Insurance Database. Eur J Intern Med 2016; 31:e8. [PMID: 26897658 DOI: 10.1016/j.ejim.2016.02.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 02/01/2016] [Accepted: 02/03/2016] [Indexed: 10/22/2022]
Affiliation(s)
- Chia-Hung Kao
- Graduate Institute of Clinical Medical Science and School of Medicine, College of Medicine, China Medical University, No. 2, Yuh-Der Road, Taichung 404, Taiwan.
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11
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Wu YT, Lee HY. National Health Insurance database in Taiwan: A resource or obstacle for health research? Eur J Intern Med 2016; 31:e9-e10. [PMID: 27079475 DOI: 10.1016/j.ejim.2016.03.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Revised: 03/18/2016] [Accepted: 03/21/2016] [Indexed: 10/22/2022]
Affiliation(s)
- Yu-Tzu Wu
- University of Exeter, Exeter, United Kingdom.
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