Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
권호 표지
DOI QR코드

DOI QR Code

Detecting Knowledge structures in Artificial Intelligence and Medical Healthcare with text mining

  • Hyun-A Lim (Department of Business Administration, Global Business School, Soonchunhyang University) ;
  • Pham Duong Thuy Vy (Department of Business Administration, Global Business School, Soonchunhyang University) ;
  • Jaewon Choi (Department of Business Administration, Global Business School, Soonchunhyang University)
  • Received : 2019.05.31
  • Accepted : 2019.09.16
  • Published : 2019.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The medical industry is rapidly evolving into a combination of artificial intelligence (AI) and ICT technology, such as mobile health, wireless medical, telemedicine and precision medical care. Medical artificial intelligence can be diagnosed and treated, and autonomous surgical robots can be operated. For smart medical services, data such as medical information and personal medical information are needed. AI is being developed to integrate with companies such as Google, Facebook, IBM and others in the health care field. Telemedicine services are also becoming available. However, security issues of medical information for smart medical industry are becoming important. It can have a devastating impact on life through hacking of medical devices through vulnerable areas. Research on medical information is proceeding on the necessity of privacy and privacy protection. However, there is a lack of research on the practical measures for protecting medical information and the seriousness of security threats. Therefore, in this study, we want to confirm the research trend by collecting data related to medical information in recent 5 years. In this study, smart medical related papers from 2014 to 2018 were collected using smart medical topics, and the medical information papers were rearranged based on this. Research trend analysis uses topic modeling technique for topic information. The result constructs topic network based on relation of topics and grasps main trend through topic.

Keywords

Acknowledgement

This research was supported by the Soonchunhyang University Research Fund.

References

  1. Alejandra, T., Viviana, G., Maria, D. P. D., and Victor, L. (2019). Sensitivity analysis of longitudinal count responses: A local influence approach and application to medical data. Journal of Applied Statistics, 46, 1021-2042.  https://doi.org/10.1080/02664763.2018.1531978
  2. Alma, J. A., Nicole, J. M., and Pablo, P. (2017). Mobile phone text messaging to improve medication adherence in secondary prevention of cardiovascular disease. The Cochrane database of systematic reviews. 
  3. Appelbaum, P. S. (2000). Threats to the confidentiality of medical records: No place to hide. Jama-journal of the American Medical Association, 283(6), 795-797.  https://doi.org/10.1001/jama.283.6.795
  4. Benites-Lazaro, L. L., Giattia, L., and Giarollab, A. (2018). Topic modeling method for analyzing social actor discourses on climate change, energy and food security. Energy Research & Social Science, 45, 318-330.  https://doi.org/10.1016/j.erss.2018.07.031
  5. Blei, D. M. (2012). Probabilistic topic models. Communication of the ACM, 55(4), 77-84.  https://doi.org/10.1145/2133806.2133826
  6. Blei, D. M., and Lafferty, J. D. (2006). Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine Learning ACM, 113-120. 
  7. Carine, B. R., Georges, B., Amir, H. E. H., and Emmanuel, A. (2018). Medical data mining for heart diseases and the future of sequential mining in medical field. Machine Learning Paradigms, 149, 71-99. 
  8. Chen, J., Ma, X., Mingxiao, D., and Zhuping, W. (2018). A blockchain application for medical information sharing. 2018 IEEE International Symposium, 1-7. 
  9. Chen, M., Yang, J., Hao, Y., Mao, S., and Hwang, K. (2017). A 5G cognitive system for healthcare. Big Data and Cognitive Computing, 1(1). 
  10. Cho, K. W., Bae, S. K., and Woo, Y. W. (2017). Analysis on topic trends and topic modeling of KSHSM journal paper using text mining. The Korean Journal of Health Service Management, 11(4), 213-224.  https://doi.org/10.12811/kshsm.2017.11.4.213
  11. Clauset, A., Newman, M. E. J., and Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6), 114-123.  https://doi.org/10.1103/PhysRevE.70.066111
  12. Dhukaram, A. V., and Baber, C. (2013). Elderly cardiac patients' medication management: Patient day-to-day needs and review of medication management system. 2013 IEEE International Conference on Healthcare Informatics, 107-114. 
  13. Dimitrov, D. V. (2016). Medical internet of things and big data in healthcare. Healthcare Informatics Research, 22(3), 156-153. 
  14. Girvan, M., and Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821-7826.  https://doi.org/10.1073/pnas.122653799
  15. Goldschmidt, P. G. (2005). HIT and MIS: implications of health information technology and medical information systems. Communications of the ACM, 48(10), 69-74.  https://doi.org/10.1145/1089107.1089141
  16. Gollakota, S., Hassanieh, H., Ransford, B., Katabi, D., and Fu, K. (2011). They can hear your heartbeats: Non-invasive security for implantable medical devices. ACM SIGCOMM Computer Communication Review, 41(4), 2-13.  https://doi.org/10.1145/2043164.2018438
  17. Goozee, R. (2013). Smart medicine: How the changing role of doctors will revolutionize health care. Journal of Mental Health, 22(6), 580-582.  https://doi.org/10.3109/09638237.2013.841875
  18. Halperin, D., Thomas, S. H., Fu, K., Tadayoshi, K., and Maisel, W. H. (2008). Security and privacy for implantable medical devices. The Community for Technology Leaders, 17(1). 
  19. Han, H., and Wang, J. (2010). Improving CNM algorithm to detect community structures of weighted network. Computing Technology Institute, 46(35), 86-89. 
  20. Hong, H. K., and Kim, S. H. (2017). Structured design of healthcare system based on mobile to improve the quality of life for the elderly people. The Society of Convergence Knowledge Transactions, 5(2),79-83. 
  21. Huang, L. C., Chu, H. C., Lien, C. Y., Hsiao, C. H., and Kao, T. (2009). Privacy preservation and information security protection for patients' portable electronic health records. In Computers in Biology and Medicine, 39(9), 743-750.  https://doi.org/10.1016/j.compbiomed.2009.06.004
  22. Jin, W., Zhongqi, Z., Kaijie, X., Yue, Y., and Ping, G. (2013). A research on security and privacy issues for patient related data in medical organization system. International Journal of Security and Its Applications, 7(4), 287-298. 
  23. Jovanov, L., Amanda, O. D., Raskovic, D., Cox, P. G., Adhami, R., and Andrasik, F. (2003). Stress monitoring using a distributed wireless intelligent sensor system emil. IEEE Engineering in Medicine & Biology Magazine, 22(3), 49-79. 
  24. Kai, F., Yanhui, R., Hui, Li., and Yintang, Y. (2018). MedBlock: Efficient and secure medical data sharing via blockchain. Journal of Medical Systems, 42(136). 
  25. Karim, A., Abderrahim, B. H., and Hayat, K. (2018). Big healthcare data: Preserving security and privacy. Journal of Big Data, 5(1). 
  26. Kim, Y. H., and Chang, H. B. (2018). The change of future environment and the task of healthcare security. OSIA S&TR Journal, 31(2), 4-9. 
  27. Lee, N. Y., and Wu, B. H. (2017). Privacy protection technology and access control mechanism for medical big data. 2017 6th IIAI International Congress on, 424-429. 
  28. Newman, M. E. J. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 70(6). 
  29. Price, W. N., and Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25, 37-43.  https://doi.org/10.1038/s41591-018-0272-7
  30. Raman, A. (2007). Enforcing privacy through security in remote patient monitoring ecosystems. 6th International Special Topic Conference, 298-301. 
  31. Ross, J. A. (1996). A security policy model for clinical information systems. 1996 IEEE Symposium, 30-43. 
  32. Silverman, B., Hanrahan, N., Bharathy, G., Gordon, K., and Johnson, D. (2015). A systems approach to healthcare: Agent-based modeling community mental health and population wellbeing. Artificial Intelligence in Medicine, 63(2), 61-71.  https://doi.org/10.1016/j.artmed.2014.08.006
  33. Smith, E., and Eloff, J. H. P. (1999). Security in health care information systems: Current trends. International Journal of Medical Information, 54(1), 39-54.  https://doi.org/10.1016/S1386-5056(98)00168-3
  34. Swaraja, K. (2019). Medical image region based watermarking for secured telemedicine. Multimedia Tools and Applications, 77, 28249-28280.  https://doi.org/10.1007/s11042-018-6020-7
  35. Sweeney, L. (2002). Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty Fuzziness and Knowledge-based System, 10(5), 571-588.  https://doi.org/10.1142/S021848850200165X
  36. Tawalbeh, L. A., Mehmood, R., Benkhlifa, E., and Song, H. (2016). Mobile cloud computing model and big data analysis for healthcare applications. Institute of Electrical and Electronics Engineers, 4, 6171-6180.  https://doi.org/10.1109/ACCESS.2016.2613278
  37. Umakanth, K., and Estefania, R. (2018). Asthma management in the era of smart-medicine: Devices, gadgets, apps and telemedicine. The Indian Journal of Pediatrics, 85(9), 757-762.  https://doi.org/10.1007/s12098-018-2611-6
  38. Wang, Y., Xue, C. A., and Wang, L. (2018). Medical information security in the era of artificial intelligence. Medical Hypotheses, 115, 58-60.  https://doi.org/10.1016/j.mehy.2018.03.023
  39. Williams, P. (2008). A practical application of CMM to medical security capability. Information Management & Computer Security, 16(1), 58-73.  https://doi.org/10.1108/09685220810862751
  40. Wissam, A., Ali, A., Suleiman, A. Y., and Abdallah, K. (2018). Smart Medicine Dispenser (SMD). 2018 IEEE 4th Middle East Conference on Biomedical Engineering, 2165-4255. 
  41. Yiwen, D., Jianwei, L., Zhenyu, G., and Hanwen, F. (2018). A medical information service platform based on distributed cloud and blockchain. 2018 IEEE International Conference on Smart Cloud, 34-39. 
  42. Yu, H. K., and Kim, J. G. (2019). Smart navigation with AI Engine for Li-Fi based medical indoor environment. Artificial Intelligence in Information and Communication(ICAIIC), 195-199. 
  43. Zahid, N., Sodhro, A. H., Fawad Zafar, R., Zahid, B., Khan, S. A., and Akhter, F. (2019). Regression-based transmission power control for green healthcare. 2019 2nd International Conference on, 1-9. 
  44. Zawadzki, P., and Krzysztof, Z. (2016). Smart product design and production control for effective mass customization in the Industry 4.0 concept. Management and Production Engineering Review, 7(3), 105-112. https://doi.org/10.1515/mper-2016-0030