Visualising and mapping the intellectual structure of medical big data

Journal of Information Science

Volumn 45, Issue 2, 2019, Pages 239-258

  Hsu, Wen Chin ^a   Li, Jia Huan ^a  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

Author keywords

Co word analysis; informetrics; medical big data; strategic diagram

Indexed keywords

BIOMEDICAL ENGINEERING; COMPUTATION THEORY; DATA HANDLING; DATA MINING; DISTRIBUTED COMPUTER SYSTEMS; HEALTH; LEARNING SYSTEMS;

CO-WORD ANALYSIS; DEGREE OF CONNECTIVITY; ELECTRONIC HEALTH RECORD SYSTEMS; INFORMETRICS; INTELLECTUAL STRUCTURES; MEDICAL TECHNOLOGIES; POTENTIAL RESEARCHES; STRATEGIC DIAGRAM;

BIG DATA;

EID: 85049986259     PISSN: 01655515     EISSN: 17416485     Source Type: Journal    
DOI: 10.1177/0165551518782824     Document Type: Article

References (50)

1. Ward JS, Barker A., Undefined by data: a survey of big data definitions, 2013, https://arxiv.org/abs/1309.5821
2. Snijders C, Matzat U, Reips U-D., ‘Big data’: big gaps of knowledge in the field of internet science. Int J Internet Sci 2012; 7: 1–5.
3. Groves P, Kayyali B, Knott D. The ‘big data’ revolution in healthcare: accelerating value and innovation, 2013, https://www.mckinsey.com/~/media/mckinsey/industries/healthcare%20systems%20and%20services/our%20insights/the%20big%20data%20revolution%20in%20us%20health%20care/the_big_data_revolution_in_healthcare.ashx (accessed 11 July 2018).
4. Ginsberg J, Mohebbi MH, Patel RS. Detecting influenza epidemics using search engine query data. Nature 2009; 457: 1012–1014.
5. Ma Y, Wang Y, Yang J. Big health application system based on health internet of things and big data. IEEE Access 2016; 5: 7885–7897.
6. Hemingway H, Feder GS, Fitzpatrick NK. Using nationwide ‘big data’ from linked electronic health records to help improve outcomes in cardiovascular diseases: 33 studies using methods from epidemiology, informatics, economics and social science in the ClinicAl disease research using LInked Bespoke studies and electronic health Records (CALIBER) programme. In: Hemingway H, Feder GS, Fitzpatrick NK. (eds) Programme grants for applied research. Southampton: NIHR Journals Library, 2017, pp. 1–330.
7. Seidl D, Schoeneborn D., System theory: the international encyclopedia of communication theory and philosophy. Hoboken, NJ: John Wiley & Sons, 2016.
8. Qiu J, Zhao R, Yang S. Informetrics: theory, methods and applications. Berlin: Springer, 2017.
9. Khasseh AA, Soheili F, Moghaddam HS. Intellectual structure of knowledge in iMetrics: a co-word analysis. Inform Process Manag 2017; 53: 705–720.
10. Yan B-N, Lee T-S, Lee T-P., Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): a co-word analysis. Scientometrics 2015; 105: 1285–1300.
11. Hu J, Zhang Y., Research patterns and trends of recommendation system in China using co-word analysis. Inform Process Manag 2015; 51: 329–339.
12. Xie P., Study of international anticancer research trends via co-word and document co-citation visualization analysis. Scientometrics 2015; 105: 611–622.
13. Garechana G, Rio B-R, Cilleruelo E. Clusterization and mapping of waste recycling science. Evolution of research from 2002 to 2012. J Assoc Inform Sci Technol 2015; 66: 1431–1446.
14. Tseng Y-H, Lin C-J, Lin Y-I., Text mining techniques for patent analysis. Inform Process Manag 2007; 43: 1216–1247.
15. Laney D., 3D data management: controlling data volume, velocity and variety. META Group Res Note 2001; 6: 70.
16. Beyer MA, Laney D., The importance of ‘big data’: a definition. Stanford, CA: Gartner, 2012.
17. Hashem IAT, Yaqoob I, Anuar NB. The rise of ‘big data’ on cloud computing: review and open research issues. Inform Syst 2015; 47: 98–115.
18. Raghupathi W, Raghupathi V., Big data analytics in healthcare: promise and potential. Health Inform Sci Syst 2014; 2: 3.
19. Chen M, Ma Y, Song J. Smart clothing: connecting human with clouds and big data for sustainable health monitoring. Mobile Netw Appl 2016; 21: 825–845.
20. Sagiroglu S, Sinanc D., Big data: a review. In: Proceedings of the 2013 international conference on collaboration technologies and systems (CTS), San Diego, CA, 20–24 May 2013, pp. 42–47. New York: IEEE.
21. Kwon O, Lee N, Shin B., Data quality management, data usage experience and acquisition intention of big data analytics. Int J Inform Manag 2014; 34: 387–394.
22. Rogers EM., Diffusion of innovations. New York: Free Press, 1995.
23. Rogers EM., A prospective and retrospective look at the diffusion model. J Health Commun 2004; 9: 13–19.
24. Rorissa A, Yuan X., Visualizing and mapping the intellectual structure of information retrieval. Inform Process Manag 2012; 48: 120–135.
25. Holmberg K, Huvila I, Kronqvist-Berg M. What is library 2.0? J Document 2009; 65: 668–681.
26. Criscuolo P, Salter A, Sheehan T., Making knowledge visible: using expert yellow pages to map capabilities in professional services firms. Res Policy 2007; 36: 1603–1619.
27. Mane KK, Börner K., Mapping topics and topic bursts in PNAS. Proc Nat Acad Sci 2004; 101: 5287–5290.
28. Kostoff RN, Schaller RR., Science and technology roadmaps. IEEE T Eng Manag 2001; 48: 132–143.
29. Murdoch TB, Detsky AS., The inevitable application of big data to health care. JAMA 2013; 309: 1351–1352.
30. Riahi A, Riahi S., The big data revolution, issues and applications. Int J Adv Res Comput Sci Soft Eng 2015; 5: 167–173.
31. Hu C-P, Hu J-M, Deng S-L. A co-word analysis of library and information science in China. Scientometrics 2013; 97: 369–382.
32. Romo-Fernández LM, Guerrero-Bote VP, Moya-Anegón F., Co-word based thematic analysis of renewable energy (1990–2010). Scientometrics 2013; 97: 743–765.
33. Ram S, Zhang W, Williams M. Predicting asthma-related emergency department visits using big data. IEEE J Biomed Health Inform 2015; 19: 1216–1223.
34. Olmeda-Gómez C, Ovalle-Perandones M-A, Perianes-Rodríguez A., Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014. Scientometrics 2017; 113: 195–217.
35. Dijcks J-P., Oracle: big data for the enterprise. Redwood City, CA: Oracle Corporation, 2014.
36. Brown B, Bughin J, Byers AH. Big data: the next frontier for innovation, competition, and productivity. Mckinsey Global Institute, 2011, https://bigdatawg.nist.gov/pdf/MGI_big_data_full_report.pdf
37. International Data Corporation (IDC). Worldwide big data technology and services 2012–2016 forecast, 2012, http://www.datascienceassn.org/sites/default/files/Worldwide%20Big%20Data%20Technology%20and%20Services%202012%20-%202016%20Forecast.pdf
38. Xie Y, Schreier G, Chang DC. Predicting days in hospital using health insurance claims. IEEE J Biomed Health Inform 2015; 19: 1224–1233.
39. Lodhi MK, Stifter J, Yao Y. Predictive modeling for end-of-life pain outcome using electronic health records. Adv Data Min 2015; 9165: 56–68.
40. Li Z, Wei Z, Yue Y. An adaptive hidden Markov model for activity recognition based on a wearable multi-sensor device. J Med Syst 2015; 39: 57.
41. Park M, Sampathkumar H, Luo B. Content-based assessment of the credibility of online healthcare information. In: Proceedings of the 2013 IEEE international conference on big data, Silicon Valley, CA, 6–9 October 2013, pp. 51–58. New York: IEEE.
42. Kawamoto R, Nazir A, Kameyama A. Hidden Markov model for analyzing time-series health checkup data. Stud Health Technol Inform 2013; 192: 491–495.
43. Adhil M, Gandham S, Talukder AK. CuraEx – clinical expert system using big-data for precision medicine. In: Proceedings of the international conference on big data analytics, Hyderabad, India, 15–18 December 2015, pp. 216–27. Berlin: Springer.
44. Dimitrov DV., Systems patientomics: the virtual in-silico patient. New Horiz Trans Med 2014; 2: 1–4.
45. Zhang P, Brusic V., Mathematical modeling for novel cancer drug discovery and development. Expert Opin Drug Dis 2014; 9: 1133–1150.
46. Lee M, Park Y-S, Kim M-H. Convergence modeling of heterogeneous medical information for acute myocardial infarction. In: Park D-S, Chao H-C, Jeong Y-S. (eds) Advances in computer science and ubiquitous computing. Berlin: Springer, 2015, pp. 757–763.
47. Weuve J, Proust-Lima C, Power MC. Guidelines for reporting methodological challenges and evaluating potential bias in dementia research. Alzheimer’s Dementia 2015; 11: 1098–1109.
48. Vogel N, Theisen C, Leidig JP. Mining mobile datasets to enable the fine-grained stochastic simulation of Ebola diffusion. Proced Comput Sci 2015; 51: 765–774.
49. Kao RR, Haydon DT, Lycett SJ. Supersize me: how whole-genome sequencing and big data are transforming epidemiology. Trend Microbio 2014; 22: 282–291.
50. Wu C-H, Liu C-H, Su P-H., Sentence extraction with topic modeling for question–answer pair generation. Soft Comput 2015; 19: 39–46.