A Systematic Approach to Healthcare Knowledge Management Systems in the Era of Big Data and Artificial Intelligence
Source abstract
Abstract
Big data in healthcare contain a huge amount of tacit knowledge that brings great value to healthcare activities such as diagnosis, decision support, and treatment. However, effectively exploring and exploiting knowledge on such big data sources exposes many challenges for both managers and technologists. In this study, we therefore propose a healthcare knowledge management system that ensures the systematic knowledge development process on various data in hospitals. It leverages big data technologies to capture, organize, transfer, and manage large volumes of medical knowledge, which cannot be handled with traditional data-processing technologies. In addition, machine-learning algorithms are used to derive knowledge at a higher level in supporting diagnosis and treatment. The orchestration of a knowledge system, big data, and artificial intelligence brings many advances to healthcare. Our research results show that the system fully ensures the knowledge development process, serving for knowledge exploration and exploitation to improve decision-making in healthcare. The knowledge system is illustrated for the detection and classification of high blood pressure and brain hemorrhage in text and CT/MRI image formats, respectively, from medical records of hospitals. It can support doctors to accurately diagnose the diseases to give appropriate treatment regimens.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Artificial Intelligence in Healthcare · Big Data and Business Intelligence · Artificial Intelligence in Healthcare and Education
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Information Technology, Vinh Long University of Technology Education, Vinh Long 85110, Vietnam
College of Information and Communication Technology, Can Tho University, Can Tho 94100, Vietnam
La Faculté Sciences et Techniques, Université de Bretagne Occidentale, 29200 Brest, France
References (27)
Show source-provided references
1. (2022, March 20). Premier Healthcare Database being used by National Institutes of of Health to Evaluate Impact of COVID-19 on Patients Across the U.S. Available online: https://www.premierinc.com/newsroom/press-releases/premier-healthcare-database-being-used-by-national-institutes-of-health-to-evaluate-impact-of-covid-19-on-patients-across-the-u-s.
2. Racial disparities in postoperative complications after radical nephrectomy: A population-based analysis10.1016/j.urology.2015.03.001 ↗
3. Adoption of robot-assisted partial nephrectomies: A population-based analysis of US surgeons from 2004 to 201310.1089/end.2017.0174 ↗
4. Changing trends in the treatment of nephrolithiasis in the real world10.1089/end.2018.0667 ↗
5. Knowledge management and knowledge management systems: Conceptual foundations and research issues10.2307/3250961 ↗
6. Knowledge management systems: Issues, challenges, and benefits
7. Knowledge management systems: Surveying the landscape10.1111/1468-2370.00054 ↗
8. Knowledge bases in the age of big data analytics
9. Begoli, E., and Horey, J. (2012, January 20–24). Design principles for effective knowledge discovery from big data. Proceedings of the 2012 Joint Working IEEE/IFIP Conference on Software Architecture and European Conference on Software Architecture, Helsinki, Finland.10.1109/WICSA-ECSA.212.32 ↗
10. Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., and Zhang, W. (2014, January 24–27). Knowledge vault: A web-scale approach to probabilistic knowledge fusion. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.10.1145/2623330.2623623 ↗
11. Knowledge management systems success in healthcare: Leadership matters10.1016/j.ijmedinf.2016.11.004 ↗
12. Causes of Challenges in Implementing Computer-Based Knowledge Management Systems in Healthcare Institutions: A Case Study of Private Hospitals in Johannesburg, South Africa
13. Manogaran, G., Thota, C., Lopez, D., Vijayakumar, V., Abbas, K.M., and Sundarsekar, R. (2017). Big data knowledge system in healthcare. Internet of Things and Big Data Technologies for Next Generation Healthcare, Springer.10.1007/978-3-319-49736-5_7 ↗
14. Le Dinh, T., Phan, T.C., and Bui, T. (2016, January 11–14). Towards an architecture for big data-driven knowledge management systems. Proceedings of the 22nd Americas Conference on Information Systems (AMCIS 2016), San Diego, CA, USA.
15. Organizational learning, knowledge and wisdom10.1108/09534810010378605 ↗
16. Enterprise content management systems as a knowledge infrastructure: The knowledge-based content management framework
17. Le Dinh, T., Van, T.H., and Moreau, É. (2014, January 27–30). A Knowledge Management Framework for Knowledge-Intensive SMEs. Proceedings of the 16th International Conference on Enterprise Information Systems ICEIS (3), Lisbon, Portugal.
18. Chambers, B., and Zaharia, M. (2018). Spark: The Definitive Guide Big Data Processing Made Simple, O’Reilly Media, Inc.. [1st ed.].
19. Singh, P. (2019). Natural Language Processing. Machine Learning with PySpark: With Natural Language Processing and Recommender Systems, Apress.10.1007/978-1-4842-4131-8 ↗
20. Kafka: A distributed messaging system for log processing
21. Gates, M. (2017). Machine Learning: For Beginners—Definitive Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple, CreateSpace Independent Publishing Platform.
22. Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7–12). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada.
23. Girshick, R. (2015, January 7–13). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Washington, DC, USA.10.1109/ICCV.2015.169 ↗
24. Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4–9). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.10.1609/aaai.v31i1.11231 ↗
25. American Heart Association (2017). What Is High Blood Pressure?, South Carolina State Documents Depository.
26. Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure10.1161/01.HYP.0000107251.49515.c2 ↗
27. Ly, N.L., and Dong, V.H. (2013). Brain Injury, Medical Publishing House.