Collaborative MARL for Real-Time Healthcare Decision Optimization
DOI:
https://doi.org/10.5281/zenodo.21455352Keywords:
Healthcare AI, Real Time, Interoperability, Decision Support, Reinforcement Learning, Multi Agent, Clinical Systems, COVID Modeling, Risk Awareness, Data Integration, Semantic Models, Uncertainty Modeling, Decision Policies, Workflow Optimization, Clinical Workflows, System Performance, Message Systems, Pipeline Architecture, Latency Reduction, Adaptive Systems.Abstract
Adaptive Real-Time Healthcare Interoperability and Decision Optimization Applied multi-agent reinforcement learning to jointly optimize adaptive real-time data integration and uncertainty-aware decision support across multiple healthcare agents. A simulated healthcare environment for a COVID-19 treatment workflow served as a multi-agent reinforcement learning environment. Adaptive real-time interoperability and decision-making were achieved. An implemented clinical testbed demonstrated improved test results and indicated the potential for improved clinical workflows. Adaptive real-time interoperability and adaptive risk-aware decision support will improve clinical workflows and support decision quality, speed, and safety while reducing the effects of gaps in the data provided by other agents, thereby facilitating agents with different data modelling paradigms to interact more efficiently and also enhancing clinical workflows.
Multiple innovations improve the usefulness and impact of multi-agent reinforcement learning on healthcare agents. The fundamental goal is to enhance the adaptive real-time interoperability and risk-aware decision-making of multiple healthcare agents in a simulated healthcare ecosystem modelled on a COVID-19 treatment workflow. Performance indications suggest that the framework achieves these goals. Automated and concurrent updates to both adaptive real-time semantic support and uncertainty-aware decision policies enable joint optimisation through multi-agent learning. The integration of message-based data transfer systems with pipeline architecture further enables multi-agent learning to address latency in the triggering of agents’ decision policies.
References
1. Allen, R. E., White Bear, J., Gupta, J. K., Kochenderfer, M. J., Zhou, Y., & Pena, J. D. (2021). Health-informed policy gradients for multi-agent reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence.
2. Bellemare, M. G., Candido, S., Castro, P. S., Gong, J., Machado, M. C., Moitra, S., Ponda, S., & Wang, Z. (2023). Foundations of reinforcement learning with applications. Journal of Artificial Intelligence Research, 76, 1–40.
3. 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.
4. Brunskill, E. (2020). Reinforcement learning for precision health. Annual Review of Biomedical Data Science, 3, 123–144.
5. Chu, T., Chinchali, S., & Katti, S. (2020). Multi-agent reinforcement learning for networked system control. arXiv Preprint arXiv:2004.01339.
6. D'Eon, G., Laroche, R., & Bellemare, M. G. (2022). Offline reinforcement learning in healthcare. Machine Learning for Healthcare Conference Proceedings.
7. Kolla, S. K. (2022). Engineering Healthcare Data Infrastructures for Predictive Clinical Analytics and Evidence-Based Decision Making. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5370-5380.
8. Gottesman, O., Johansson, F., Meier, J., Dent, J., Lee, D., Srinivasan, S., Zhang, L., Ding, Y., Wihl, D., Peng, X., Yao, J., Lage, I., Mosch, C., Mark, R., & Doshi-Velez, F. (2020). Evaluating reinforcement learning algorithms in healthcare. Nature Medicine, 26(1), 16–18.
9. Guez, A., Mirza, M., Gregor, K., Kabra, R., Racanière, S., Weber, T., Raposo, D., Santoro, A., Orseau, L., Eccles, T., Wayne, G., Silver, D., & Lillicrap, T. (2021). Offline reinforcement learning for healthcare decision making. Advances in Neural Information Processing Systems.
10. Komorowski, M., Gordon, A., Celi, L. A., & Faisal, A. A. (2020). Artificial intelligence for intensive care medicine. Nature Reviews Nephrology, 16(12), 706–722.
11. Lee, H. R., & Lee, T. (2021). Multi-agent reinforcement learning algorithm to solve a partially observable multi-agent problem in disaster response. European Journal of Operational Research, 291(1), 296–308.
12. Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.
13. Li, J., Liu, H., Li, X., & Wang, F. (2022). Reinforcement learning in intelligent healthcare systems: A review. Artificial Intelligence Review, 55(5), 3453–3490.
14. Liu, Y., Wei, W., Sun, Y., & Zhang, X. (2023). Multi-agent deep reinforcement learning: A survey. IEEE Transactions on Neural Networks and Learning Systems.
15. Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2020). Multi-agent actor-critic for mixed cooperative-competitive environments. Artificial Intelligence Journal, 285, 103296.
16. Amistapuram, K. Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
17. Ma, T., Yu, C., & Liu, J. (2023). Reinforcement learning for clinical decision support systems. Journal of Biomedical Informatics, 141, 104362.
18. Munavalli, J. R., Rao, S. V., Srinivasan, A., & van Merode, G. G. (2020). An intelligent real-time scheduler for outpatient clinics: A multi-agent system model. Health Informatics Journal, 26(4), 2385–2404.
19. Nemati, S., Ghassemi, M., & Clifford, G. D. (2021). Optimal medication dosing from reinforcement learning. Nature Digital Medicine, 4, 58.
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. Oliehoek, F. A., & Amato, C. (2020). A concise introduction to decentralized POMDPs. Springer.
22. Pan, S., Yang, Q., & Yu, P. S. (2021). Federated learning for healthcare informatics. ACM Computing Surveys, 54(5), 1–37.
23. Pesce, E., & Montana, G. (2023). Learning multi-agent coordination through connectivity-driven communication. Machine Learning, 112(2), 483–514.
24. Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
25. Raghu, A., Komorowski, M., Ahmed, I., Celi, L., Szolovits, P., & Ghassemi, M. (2021). Deep reinforcement learning for sepsis treatment. Machine Learning for Healthcare Conference Proceedings.
26. Shalev-Shwartz, S., & Ben-David, S. (2020). Understanding machine learning: From theory to algorithms. Cambridge University Press.
27. Silver, D., Singh, S., Precup, D., & Sutton, R. S. (2021). Reward is enough. Artificial Intelligence, 299, 103535.
28. Sutton, R. S., & Barto, A. G. (2020). Reinforcement learning: An introduction (2nd ed.). MIT Press.
29. 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.
30. Tang, Y., Wang, J., & Liu, H. (2022). Deep reinforcement learning in medical decision making. Knowledge-Based Systems, 250, 109052.
31. Topol, E. (2020). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 26(1), 44–56.
32. Wang, F., Casalino, L. P., & Khullar, D. (2021). Deep learning in medicine—Promise, progress, and challenges. JAMA Internal Medicine, 181(4), 454–456.
33. Wang, J., & Yang, Y. (2020). An overview of multi-agent reinforcement learning from a game-theoretical perspective. arXiv Preprint arXiv:2011.00583.
34. 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.
35. Wang, Z., & Feng, X. (2023). Reinforcement learning for healthcare operations optimization. Expert Systems with Applications, 220, 119664.
36. Wu, Q., & Wang, X. (2022). Multi-agent collaboration for intelligent healthcare. IEEE Access, 10, 57324–57341.
37. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
38. Xie, Y., Wang, Y., & Zhao, L. (2021). Artificial intelligence in hospital management. Healthcare, 9(5), 573.
39. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2020). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 12.
40. Yu, C., Liu, J., Nemati, S., & Yin, G. (2021). Reinforcement learning in healthcare: A survey. ACM Computing Surveys, 55(1), 1–36.
41. Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
42. Zhang, C., Li, Y., & Wang, H. (2023). Explainable reinforcement learning for healthcare applications. Artificial Intelligence in Medicine, 139, 102537.
43. Zhang, K., Yang, Z., & Başar, T. (2021). Multi-agent reinforcement learning: A selective overview of theories and algorithms. Handbook of Reinforcement Learning and Control, 321–384.
44. Zhang, X., & Lu, H. (2022). Intelligent decision support systems using reinforcement learning. Information Sciences, 607, 1323–1340.
45. Zhou, Y., Gupta, J. K., & Kochenderfer, M. J. (2021). Cooperative reinforcement learning for distributed systems. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5461–5473.
46. Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
47. Agarwal, R., Schwarzer, M., Castro, P. S., Courville, A., & Bellemare, M. G. (2021). Deep reinforcement learning at the edge of the statistical precipice. Advances in Neural Information Processing Systems, 34, 29304–29320.
48. Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2020). Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 37(6), 26–38.
49. Bohr, A., & Memarzadeh, K. (2020). Artificial intelligence in healthcare. Academic Press.
50. Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
51. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2021). A guide to deep learning in healthcare. Nature Medicine, 27(1), 24–29.
52. Ghassemi, M., Oakden-Rayner, L., & Beam, A. (2021). The false hope of current approaches to explainable AI in healthcare. The Lancet Digital Health, 3(11), e745–e750.
53. Holzinger, A., Saranti, A., Molnar, C., Biecek, P., & Samek, W. (2022). Explainable AI methods for healthcare. Nature Reviews Methods Primers, 2, 82.
54. Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1-14.
55. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2020). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 5(2), 230–243.
56. Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (2020). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101, 99–134.
57. Kiumarsi, B., Vamvoudakis, K. G., Modares, H., & Lewis, F. L. (2020). Optimal and autonomous control using reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 31(11), 4402–4416.
58. Mangala, N. (2021). CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps. Universal Journal of Business and Management, 1(1), 1-18.
59. Long, Q., Wu, X., & Li, S. (2023). Deep reinforcement learning for smart healthcare systems. Future Generation Computer Systems, 142, 188–201.
60. Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2020). Resource management with deep reinforcement learning. Proceedings of the ACM Workshop on Hot Topics in Networks.
61. Mirchev, M., & Petrov, P. (2022). Multi-agent artificial intelligence in healthcare ecosystems. Electronics, 11(19), 3168.
62. 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.
63. Nguyen, T. T., Nguyen, N. D., & Nahavandi, S. (2020). Deep reinforcement learning for multi-agent systems: A review. IEEE Transactions on Neural Networks and Learning Systems, 31(5), 1400–1423.
64. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., et al. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.
65. Peng, X., Wang, H., & Li, Y. (2022). Intelligent healthcare optimization using reinforcement learning. Computers in Biology and Medicine, 149, 105982.
66. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
67. Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., et al. (2020). The future of digital health with federated learning. npj Digital Medicine, 3, 119.
68. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2021). Deep learning in electronic health records. Journal of Biomedical Informatics, 123, 103907.
69. Silva, A., Ribeiro, B., & Costa, C. A. (2023). Intelligent clinical decision support using collaborative AI. Artificial Intelligence in Medicine, 141, 102566.
70. Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
71. Sun, H., Wang, X., & Li, P. (2023). Distributed reinforcement learning for healthcare optimization. Knowledge-Based Systems, 275, 110677.
72. Tang, C., Yang, J., & Zhao, X. (2022). Multi-agent collaboration in healthcare cyber-physical systems. IEEE Internet of Things Journal, 9(18), 17020–17033.
73. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
74. Wang, S., Jiang, X., Singh, A., & Wang, L. (2021). Intelligent hospital management using AI techniques. IEEE Access, 9, 116547–116561.
75. Xu, Y., Li, C., & Zhao, D. (2023). Deep multi-agent reinforcement learning for collaborative decision optimization. Information Sciences, 639, 119019.
76. Yang, Z., Zhang, K., & Başar, T. (2020). Multi-agent reinforcement learning in networked systems. Annual Reviews in Control, 49, 1–16.
77. Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
78. Zhang, Y., Wang, L., & Chen, H. (2022). AI-enabled clinical decision support for real-time healthcare. Journal of Biomedical Informatics, 130, 104091.
79. Zhou, Z., Wang, J., Li, X., & Chen, Y. (2023). Collaborative artificial intelligence for healthcare decision optimization. Expert Systems with Applications, 225, 120058.
80. Bandi, V. D. V. K. Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics.
Additional Files
Published
Issue
Section
License
Copyright (c) 2026 European Advanced Journal for Science & Engineering (EAJSE)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Articles published in the European Advanced Journal for Science & Engineering (EAJSE) are made freely available online immediately upon publication under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is properly cited. Authors retain copyright of their work. By submitting to EAJSE, authors grant the journal the right of first publication. For details, visit: https://creativecommons.org/licenses/by/4.0/
