Visualizing the knowledge structure and evolution of big data research in healthcare informatics

International Journal of Medical Informatics

Volumn 98, Issue , 2017, Pages 22-32

  Gu, Dongxiao ^a   Li, Jingjing ^a   Li, Xingguo ^a   Liang, Changyong ^a,b  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^b MOE Engineering Research Center of Intelligent Decision Making and Information System Technology   (China)

Author keywords

Bibliometrics; Big data; Healthcare informatics; Knowledge management; Knowledge structure

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; DATA VISUALIZATION; DIAGNOSIS; DISEASES; HEALTH CARE; INFORMATION MANAGEMENT; INFORMATION SCIENCE; KNOWLEDGE MANAGEMENT; LEARNING SYSTEMS; NURSING;

BIBLIOMETRIC ANALYSIS; BIBLIOMETRICS; CO-OCCURRENCE ANALYSIS; FUTURE RESEARCH DIRECTIONS; HEALTH CARE INFORMATICS; KNOWLEDGE STRUCTURES; LITERATURE CONTENT ANALYSIS; PEOPLE'S REPUBLIC OF CHINA;

BIG DATA;

BIBLIOMETRICS; CHINA; CONTENT ANALYSIS; DATA MINING; EARLY DIAGNOSIS; EVOLUTION; GERMANY; HEALTH CARE; HEALTH PROMOTION; HEALTH SERVICE; HUMAN; KNOWLEDGE BASE; KNOWLEDGE MANAGEMENT; MACHINE LEARNING; MEDICAL INFORMATICS; MEDICAL INFORMATION; MEDICAL LITERATURE; MEDICAL RESEARCH; NURSING CARE; PRIORITY JOURNAL; PROGNOSIS; QUALITY OF LIFE; REVIEW; STRUCTURE ANALYSIS; TREATMENT PLANNING; UNITED KINGDOM; UNITED STATES; DATA BASE; PROCEDURES;

BIBLIOMETRICS; BIOMEDICAL RESEARCH; DATA MINING; DATABASES AS TOPIC; HUMANS; MEDICAL INFORMATICS;

EID: 85001033133     PISSN: 13865056     EISSN: 18728243     Source Type: Journal    
DOI: 10.1016/j.ijmedinf.2016.11.006     Document Type: Review

References (63)

1. [1] Khoury, M.J., Ioannidis, J.P., Big data meets public health. Science 346:6213 (2014), 1054–1055.
2. [2] Mashey, J.R., Big Data and the Next Wave of Infrastress. In Computer Science Division Seminar. 1997, University of California, Berkeley.
3. [3] Mauro, A.D., Greco, M., Grimaldi, M., What is big data? a consensual definition and a review of key research topics. AIP Conf. Proc. 1644:1 (2015), 97–104.
4. [4] Larson, D., Chang, V., A review and future direction of agile, business intelligence, analytics and data science. Int. J. Inf. Manage. 36 (2016), 700–710.
5. [5] Davenport, T.H., Big data at work: dispelling the myths. Uncovering the Opportunities Big Data Bootcamp, 2014, 49–62.
6. [6] Ebrahimi, M., Mohan, A., Kashlev, A., Lu, S., BDAP: a big data placement strategy for cloud-Based scientific workflows. IEEE First International Conference on Big Data Computing Service and Applications, IEEE Computer Society, 2015, 105–114.
7. [7] Baro, E., Degoul, S., Beuscart, R., Chazard, E., Toward a literature-driven definition of big data in healthcare. BioMed Res. Int. 15:4 (2015), 1–9.
8. [8] Kuo, M.H., Sahama, T., Kushniruk, A.W., Borycki, E.M., Grunwell, D., Health big data analytics: current perspectives, challenges and potential solutions. Int. J. Big Data Intell. 2 (2014), 114–126.
9. [9] Lynch, C., Big data: how do your data grow?. Nature 455 (2008), 28–29.
10. [10] Kum, H.C., Ahalt, S., Carsey, T.M., Dealing with data: governments’ records. Science, 332, 2011, 1263.
11. [11] Li, G., Research status and scientific thinking of big data. Bull. Chin. Acad. Sci. 27:6 (2012), 647–657.
12. [12] Kim, G.H., Trimi, S., Chung, J.H., Big data applications in the government sector: a comparative analysis among leading countries. Commun. ACM 57:3 (2014), 78–85.
13. [13] Medeiros, B.C., Satram-Hoang, S., Hurst, D., Hoang, K.Q., Momin, F., Reyes, C., Big data analysis of treatment patterns and outcomes among elderly acute myeloid leukemia patients in the United States. Ann. Hematol. 94:7 (2015), 1127–1138.
14. [14] Zhang, Y., Guo, S.L., Han, L.N., et al. Application and exploration of big data mining in clinical medicine. Chin. Med. J. (Engl.) 129:6 (2015), 731–738.
15. [15] Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 165 (2015), 234–246.
16. [16] Raghupathi, W., Raghupathi, V., Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2:1 (2014), 1–10.
17. [17] Luo, J., Wu, M., Gopukumar, D., Zhao, Y., Big data application in biomedical research and health care: a literature review. Biomed. Inf. Insights 8 (2016), 1–10.
18. [18] Bates, D.W., Saria, S., Ohnomachado, L., Shah, A., Escobar, G., Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. (Millwood) 33:7 (2014), 1123–1131.
19. [19] Khoury, M.J., Ioannidis, J.P.A., Big data meets public health. N. Z. Med. J. 93:676 (2014), 1054–1055.
20. [20] Roski, J., Bolinn, G.W., Andrews, T.A., Creating value in health care through big data: opportunities and policy implications. Health Aff. (Millwood) 33:7 (2014), 1115–1122.
21. [21] Craven, M., Page, C.D., Big data in healthcare: opportunities and challenges. Big Data 3:4 (2015), 209–210.
22. [22] White, S.E., A review of big data in healthcare: challenges and opportunities. Open Access Bioinform.(6), 2014, 13–18.
23. [23] Gaitanou, P., Garoufallou, E., Balatsoukas, P., The Effectiveness of Big Data in Health Care: A Systematic Review. Metadata and Semantics Research. 2014, Springer International Publishing.
24. [24] Felizardo, K.R., Macdonell, S.G., Mendes, E., Maldonado, J.C., A systematic mapping on the use of visual data mining to support the conduct of systematic literature reviews. J. Softw. 7:2 (2012), 450–461.
25. [25] Gagnon, M.P., Desmartis, M., Labrecque, M., et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J. Med. Syst. 36:1 (2012), 241–277.
26. [26] Thakur, S., Ramzan, M., A systematic review on cardiovascular diseases using big-data by Hadoop. International Conference – Cloud System and Big Data Engineering, IEEE, 2016.
27. [27] Chiarini, G., Ray, P., Akter, S., Masella, C., Mhealth technologies for chronic diseases and elders: a systematic review. IEEE J. Sel. Areas Commun. 31:9 (2013), 6–18.
28. [28] Good, D.J., McIntyre, C.M., Use of journal clubs within senior capstone courses: analysis of perceived gains in reviewing scientific literature. J. Nutr. Educ. Behav. 47:5 (2015), 477–479 (e1).
29. [29] Balaid, A., Rozan, M.Z.A., Hikmi, S.N., Memon, J., Knowledge maps: a systematic literature review and directions for future research. Int. J. Inf. Manage. 36:3 (2016), 451–475.
30. [30] No, H.J., An, Y., Park, Y., A structured approach to explore knowledge flows through technology-based business methods by integrating patent citation analysis and text mining. Technol. Forecast. Soc. Change 97 (2014), 181–192.
31. [31] Pan, L., Wang, S., A bibliometrics analysis on Chinese education research hotspots based on literature keywords co-occurrence knowledge map. Educ. Res. Exp. 6 (2011), 20–24.
32. [32] Condic, K.S., Citation analysis of student dissertations and faculty publications in reading and educational leadership at oakland university. J. Acad. Librariansh. 41:5 (2015), 548–557.
33. [33] Zhang, X., Gao, Y., Yan, X., Pablos, P.O.D., Sun, Y., Cao, X., From e-learning to social-learning: mapping development of studies on social media-supported knowledge management. Comput. Hum. Behav. 51 (2015), 803–811.
34. [34] Lin, Z., Wu, C., Hong, W., Visualization analysis of ecological assets/values research by knowledge mapping. Acta Ecol. Sin. 35:5 (2015), 142–154.
35. [35] Xu, Q., Zhang, W., Hu, L., Wang, J., Jin, J., The development and research of bioinformatics in neuroscience. Aasri Procedia 1:3 (2012), 359–364.
36. [36] Liu, Q., A study on mining bibliographic records by designed software sati: case study on library and information science. J. Inf. Resour. Manage. 2:1 (2012), 50–58.
37. [37] Afendi, F.M., Ono, N., Nakamura, Y., Nakamura, K., Darusman, L.K., Kibinge, N., et al. Data mining methods for omics and knowledge of crude medicinal plants toward big data biology. Computat. Struct. Biotechnol. J. 4:5 (2013), 1–14.
38. [38] Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al. Big Data: The Next Frontier for Innovation, Competition, and Productivity. 2011, McKinsey & Company, New York.
39. [39] Bornmann, L., Marx, W., Histcite analysis of papers constituting the h, index research front. J. Informetr. 6:2 (2012), 285–288.
40. [40] Ebadi, A., Schiffauerova, A., How to become an important player in scientific collaboration networks?. J. Informetr. 9:4 (2015), 809–825.
41. [41] Lin, Z., Wu, C., Hong, W., Visualization analysis of ecological assets/values research by knowledge mapping. Acta Ecol. Sin. 35:5 (2015), 142–154.
42. [42] Choe, H., Lee, D.H., Kim, H.D., Seo, I.W., Structural properties and inter-organizational knowledge, flows of patent citation network: the case of organic solar cells. Renew. Sustain. Energy Rev. 55 (2016), 361–370.
43. [43] Chen, C.M., Citespace ii: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 57:3 (2009), 359–377.
44. [44] Small, H., Co-citation in the scientific literature: a new measure of the relationship between two documents. J. Am. Soc. Inf. Sci. 24:24 (1973), 265–269.
45. [45] Navonil, M., Nik, B., Simon, J.E.T., Stelios, S., Exploring the e-science knowledge base through co-citation analysis ☆. Procedia Comput. Sci. 19 (2013), 586–593.
46. [46] Hsiao, C.H., Yang, C., The intellectual development of the technology acceptance model: a co-citation analysis. Int. J. Inf. Manage. 31:2 (2011), 128–136.
47. [47] Backhaus, K., Kai, L., Koch, M., The structure and evolution of business-to-business marketing: a citation and co-citation analysis. Ind. Mark. Manage. 40:6 (2011), 940–951.
48. [48] Chang, R.M., Kauffman, R.J., Kwon, Y.O., Understanding the paradigm shift to computational social science in the presence of big data. Decis. Support Syst. 63:3 (2014), 67–80.
49. [49] Özçınar, H., Mapping teacher education domain: a document co-citation analysis from 1992 to 2012. Teach. Teach. Educ. 47 (2015), 42–61.
50. [50] Sankey, H., Scientific realism and the semantic incommensurability thesis. Stud. Hist. Philos. Sci. Part A 40:2 (2009), 196–202.
51. [51] Callon, M., Courtial, J.J.P., Turner, W.A., Bauin, S., From translations to problematic networks—an introduction to co-word analysis. soc sci inf sur les sci soc. Soc. Sci. Inf. 22:2 (1983), 191–235.
52. [52] Sandouk, U., Chen, K., Learning contextualized semantics from co-occurring terms via a siamese architecture. Neural Netw. 76:C (2015), 65–96.
53. [53] Wu, C.C., Leu, H.J., Examining the trends of technological, development in hydrogen energy using patent co-word map analysis. Int. J. Hydrogen Energy 39:33 (2014), 19262–19269.
54. [54] Li, H.J., An, H.Z., Wang, Y., Huang, J.C., Gao, X.Y., Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: based on two-mode affiliation network. Phys. A Stat. Mech. Appl. 450 (2016), 657–669.
55. [55] Huenteler, J., Ossenbrink, J., Schmidt, T.S., Hoffmann, V.H., How a product's design hierarchy shapes, the evolution of technological knowledge-evidence from patent-citation networks in wind power. Soc. Sci. Electron. Publ. 45:6 (2014), 1195–1217.
56. [56] Xie, Z., Ouyang, Z.Z., Liu, Q.M., Li, J.P., A geometric graph model for citation networks of exponentially growing scientific papers. Phys. A-Stat. Mechan. Appl. 456 (2016), 167–175.
57. [57] Katie, L.S., Thomas, B., Andrew, P., Bordetella pertussis epidemiology and evolution in the light of pertussis resurgence. Infect. Genet. Evol. 40 (2016), 136–143.
58. [58] Lynch, J., Smith, G.D., A life course approach to chronic disease epidemiology. Annu. Rev. Public Health 26 (2005), 1–35.
59. [59] Krätke, S., Regional knowledge networks: a network analysis approach to the interlinking of knowledge resources. Eur. Urban Reg. Stud. 17:1 (2010), 83–97.
60. [60] Chawla, N.V., Davis, D.A., Bringing big data to personalized healthcare: a patient-centered framework. J. Gen. Intern. Med. 28:3 (2013), 660–665.
61. [61] Schadt, E.E., Linderman, M.D., Sorenson, J., Lee, L., Nolan, G.P., Cloud and heterogeneous computing solutions exist today for the emerging big data problems in biology. Nat. Rev. Genet., 12(3), 2011, 224.
62. [62] O'Driscoll, A., Daugelaite, J., Sleator, R.D., ’Big data‘, Hadoop and cloud computing in genomics. J. Biomed. Inform. 46:5 (2013), 774–781.
63. [63] Sahoo, S.S., Jayapandian, C., Garg, G., Kaffashi, F., Chung, S., Bozorgi, A., et al. Heart beats in the cloud: distributed analysis of electrophysiological ‘Big Data’ using cloud computing for epilepsy clinical research. J. Am. Med. Inform. Assoc. 21:2 (2014), 263–271.