Multicloud Data Fabric Architectures for Scalable Healthcare and Finance AI Systems
Keywords:
multicloud, data fabric, healthcare AI, finance AIAbstract
Temporal sequence modelling and claims-based analytics help deep learning pipelines forecast public and commercial health system illness costs. Hybrid LSTM networks and temporal CNNs represent chronic and infectious disease development's intricate temporal connections and nonlinear cost dynamics. Data on electronic health records, insurance claims, demographics, and resource use predict multi-institutional healthcare costs and financial risk. Hospital and payer system interpretation and scalability are real-time with feature attribution and modular pipelines. Policy and finance benefit from foresight, cost spike identification, and resource allocation optimisation. Health economics cost-control and risk-management benefit from deep temporal models.
Downloads
References
Ghemawat, S., Gobioff, H., & Leung, S. T. (2016). The Google file system. Communications of the ACM, 59(3), 29–43.
Armbrust, M., et al. (2016). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
Buyya, R., Calheiros, R. N., & Dastjerdi, A. V. (2016). Big data: Principles and paradigms. Morgan Kaufmann.
Zhang, Q., Chen, M., Li, L., & Kim, M. (2017). Hybrid and multicloud computing: Models, architectures, and challenges. IEEE Cloud Computing, 4(3), 14–26.
Ranjan, R., et al. (2017). Data-intensive cloud computing: A survey. IEEE Transactions on Cloud Computing, 5(1), 135–156.
Hashem, I. A. T., et al. (2017). The role of big data in smart healthcare: A survey. Future Generation Computer Systems, 72, 306–324.
McAfee, A., & Brynjolfsson, E. (2017). Machine learning in finance: Economic impacts and challenges. Harvard Business Review, 95(4), 60–68.
Pahl, C. (2017). Containerization and the PaaS cloud. IEEE Cloud Computing, 2(3), 24–31.
Bonomi, F., Milito, R., Natarajan, P., & Zhu, J. (2017). Fog computing architecture for data-intensive AI systems. Proceedings of the MCC Workshop.
Chen, M., Mao, S., & Liu, Y. (2018). Big data analytics: Challenges, open research issues and tools. IEEE Communications Surveys & Tutorials, 16(2), 120–137.
Rajkomar, A., Dean, J., & Kohane, I. (2018). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358.
Kuo, A. M. H. (2018). Opportunities and challenges of cloud computing in healthcare. Journal of Medical Internet Research, 13(3), e67.
Basel Committee on Banking Supervision. (2018). Sound practices: Implications of fintech developments for banks and bank supervisors. Bank for International Settlements.
Demchenko, Y., Grosso, P., de Laat, C., & Membrey, P. (2018). Addressing big data challenges for scientific data infrastructure. IEEE Cloud Computing, 1(3), 34–39.
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2018). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
Jagadish, H. V., et al. (2018). Big data and its technical challenges. Communications of the ACM, 57(7), 86–94.
Amazon Web Services. (2019). Architecting for the cloud: AWS best practices. AWS Whitepaper.
Microsoft Azure. (2019). Building resilient multi-cloud architectures for regulated industries. Microsoft Technical Whitepaper.
Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25, 24–29.
Heaton, J., Polson, N., & Witte, J. (2019). Deep learning in finance. Applied Stochastic Models in Business and Industry, 33(1), 3–12.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
