An Explainable AI Framework for Predictive Healthcare Analytics in Cloud-Native Smart Environments
Abstract
The vision for a cloud-native architecture capable of supporting data exchange and predictive analytics across deployed smart health systems relies on two accepted concepts: (i) interoperability, the ability of an information technology system or product to work with other information technology systems or products; and (ii) predictive analytics, technology that analyzes current and historical facts to make predictions about future events. The proposed architectural framework focuses on data governance, and in particular the aspects of provenance, privacy, and analysis methods. A privacy-preserving approach to data classification detects and replaces sensitive information elements such as social security numbers, addresses, or credit card numbers. The management of data exchange operations is aligned with the requirements of the health domain data model. Predictive analytics use Explainable Artificial Intelligence techniques to provide interpretable predictions for clinical users.
The healthcare domain is one of the sectors where the use of smart systems has been accelerated because of the requirements imposed by the COVID-19 pandemic. Many Cloud-Native Smart Systems are designed, implemented, and deployed on the Cloud to support different Health software applications. Such systems, although deployed in the Cloud, do not have the ability to interact with each other owing to a lack of interoperability. Large amounts of data are collected from different users and devices; the creation of predictive models is an important task to help detect patterns, trends, and predict future values in health, analyzing patients' data to decide the presence of a health problem. A Cloud-Native architecture able to perform predictive analytics in the health domain and that supports interoperability among data is needed.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 3 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
12102-12116 |
Published |
May 14, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Sasi Kumar Kolla (%2024). An Explainable AI Framework for Predictive Healthcare Analytics in Cloud-Native Smart Environments. International Journal of Science, Research and Technology , Vol. 7 No. 3 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 12102-12116. https://doi.org/10.15662/IJSRAT.2024.0703004 |
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3. Mangalampalli, B. M. (2023). AI-Driven Anomaly Detection in Healthcare Claims Data: A Business Intelligence Perspective. Journal of Rare Cardiovascular Diseases.
4. Kolla, T., & Kolla, S. K. (2023). FHIR-Based Real-Time Healthcare Analytics using Unsupervised Learning. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11751.
5. Allgaier, J., Mulansky, L., Draelos, R. L., & Pryss, R. (2023). How does the model make predictions? A systematic literature review on the explainability power of machine learning in healthcare. Artificial Intelligence in Medicine, 143, 102616.
6. Davuluri, P. S. L. N. (2023). Integrating artificial intelligence into event-driven financial crime compliance platforms. International Journal of Finance, 36(6), 707-736.
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9. Bienefeld, N., Boss, J. M., Lüthy, R., Brodbeck, D., Azzati, J., Blaser, M., Willms, J., & Keller, E. (2023). Solving the explainable AI conundrum by bridging clinicians’ needs and developers’ goals. npj Digital Medicine, 6, 94.
10. Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN.
11. Kolla, S. H. (2022). Strategic Information Integration Models for Cross-Functional Service Optimization in Large-Scale Enterprises. International Journal of Emerging Trends in Engineering and Management Research, 7(3), 11811.
12. Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
13. Cremonesi, F., Planat, V., Kalokyri, V., Kondylakis, H., Sanavia, T., Resinas, V. M. M., Singh, B., & Uribe, S. (2023). The need for multimodal health data modeling: A practical approach for a federated-learning healthcare platform. Journal of Biomedical Informatics, 141, 104338.
14. Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Universal Journal of Business and Management.
15. Di Martino, F., & Delmastro, F. (2023). Explainable AI for clinical and remote health applications: A survey on tabular and time series data. Artificial Intelligence Review, 56, 5261–5315.
16. Vijaya Rama Raju Gottimukkala, “Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2020.91225
17. Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
18. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248-260.
19. Hulsen, T. (2023). Explainable artificial intelligence (XAI): Concepts and challenges in healthcare. AI, 4(3), 652–666.
20. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
21. Bandi, V. D. V. K. (2023, November). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205.
22. Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20.
23. Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
24. Munjal, K., & Bhatia, R. (2023). A systematic review of homomorphic encryption and its contributions in healthcare industry. Complex & Intelligent Systems, 9, 3759–3786.
25. Reddy, V. A. R. (2023). Orchestrating the Future Autonomous Healthcare Data Pipeline Management through Agentic AI Architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7979-7992.
26. Kaulwar, P. K., Pamisetty, A., Mashetty, S., Adusupalli, B., & Pandiri, L. (2023). Harnessing intelligent systems and secure digital infrastructure for optimizing housing finance, risk mitigation, and enterprise supply networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 372-402.
27. Nambiar, A. A., S., H., & S., S. (2023). Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data. Frontiers in Artificial Intelligence, 6, 1272506.
28. Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
29. Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.
30. Navakauskas, D., & Kazlauskas, M. (2023). Fog computing in healthcare: Systematic review. Informatica, 34(3), 577–602.
31. Aitha, A. R. (2021). Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks.
32. Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
33. Nazir, S., Dickson, D. M., & Akram, M. U. (2023). Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks. Computers in Biology and Medicine, 156, 106668.
34. Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows. Universal Journal of Finance and Economics.
35. Kolla, S. H. (2023). Large Language Model-Driven Enterprise Service Intelligence for Digital Workflow Transformation. International Journal of Research and Applied Innovations, 6(1), 8380-8391.
36. Reddy, K. D., & Gadekallu, T. R. (2023). A comprehensive survey on federated learning techniques for healthcare informatics. Computational Intelligence and Neuroscience, 2023, 8393990.
37. Segireddy, A. R. (2021). Containerization and Microservices in Payment Systems: A Study of Kubernetes and Docker in Financial Applications. Universal Journal of Business and Management, 1(1), 1-17.
38. Amistapuram, K. (2021). Digital Transformation in Insurance: Migrating Enterprise Policy Systems to. NET Core. Universal Journal of Computer Sciences and Communications, 1(1), 1-17.
39. Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
40. DAVULURI, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking. Kurdish Studies.
41. Torab-Miandoab, A., Samad-Soltani, T., Jodati, A., & Rezaei-Hachesu, P. (2023). Interoperability of heterogeneous health information systems: A systematic literature review. BMC Medical Informatics and Decision Making, 23, 18.
42. Divya, V., & Bandi, V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 78.
43. Mattaparthi, R. (2023). Deep Learning-Driven Combustion Anomaly Detection in Diesel Powertrains: A Multi-Sensor Fusion Approach for Real-Time ECM Adaptation. International Journal of Intelligent Systems and Applications in Engineering, 11, 1084.
44. Xu, Q., Xie, W., Liao, B., Hu, C., Qin, L., Yang, Z., Xiong, H., Lyu, Y., Zhou, Y., & Luo, A. (2023). Interpretability of clinical decision support systems based on artificial intelligence from technological and medical perspective: A systematic review. Journal of Healthcare Engineering, 2023, 9919269.
45. Kolla, S. K. (2023). Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7955-7966.
46. Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
47. Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
48. Atanda, A. (2023). Cloud computing in healthcare industry: A systematic literature review. Global Journal of Information Technology: Emerging Technologies, 13(2), 64–71.
49. Reddy, V. A. R. (2023). Predictive Healthcare Administration Using Advanced Payer Analytics and Population Health Data Engineering. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7967-7978.
50. Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.
51. Mashetty, S. (2023). Leveraging Data Analytics to Enhance Affordable Housing Initiatives and Community Development. Available at SSRN 5249221.
52. Arega, A., & Sharma, D. P. (2023). Towards smart and green features of cloud computing in healthcare services: A systematic literature review. Journal of Information Systems Engineering and Business Intelligence, 9(2), 161–180.
53. Loganathan, R. (2021). Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach. Universal Journal of Computer Sciences and Communications, 1(1), 1-26.
54. Kolla, S. H., & Loganathan, R. (2023). Cloud-Native Deep Learning Architectures For Secure Generative AI Deployment In Enterprise Workflow Platforms. Journal of International Crisis and Risk Communication Research, 603-618.
55. Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN.
56. Fathullah, M. A., Subbarao, A., & Muthaiyah, S. (2023). A systematic review: Risk management of cloud computing projects in healthcare. International Journal of Management, Finance and Accounting, 4(2), 83–115.
57. Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
58. Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
59. Reddy, V. A. R. (2021). Challenges in Standardizing Member Eligibility Data Across Multi-Payer Healthcare Ecosystems. International Journal of Medical Toxicology and Legal Medicine, 24(3), 1-19.
60. Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1-13.
61. Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.
62. Bandi, V. D. V. K. (2023). MLOps frameworks for reliable model deployment in cloud data platforms. Journal of Artificial Intelligence and Big Data, 3(1), 84-101.
63. Mangalampalli, B. M. (2021). Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. World Journal of Clinical Medicine Research, 1(1), 1-18.
64. Mashetty, S. (2023). A Comparative Analysis of Patented Technologies Supporting Mortgage and Housing Finance. Available at SSRN 5249181.
65. Aitha, A. R. (2021). Dev Ops Driven Digital Transformation: Accelerating Innovation In The Insurance Industry. Journal of International Crisis and Risk Communication Research.
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