Autonomous Healthcare Analytics via Graph Neural Networks and Generative AI
Keywords:
FHIR-Based Healthcare Analytics, Generative Adversarial Networks, Graph Neural Networks, Clinical Decision Support, Healthcare Data Interoperability, FHIR Data Integration, AI in Healthcare Analytics, Clinical Inference Engines, Healthcare Data Standardization, Predictive Clinical Modeling, Healthcare Graph Analytics, AI-Driven Diagnostics, Clinical Data Normalization, Healthcare AI Frameworks, Medical Data Networks, Quality Assessment in AI, Synthetic Clinical Data Generation, Interoperable Health Systems, Data-Driven Clinical Insights, Advanced Healthcare Analytics.Abstract
The rapidly evolving landscape of healthcare analytics demands effective ways to operationalize the growing volume of interoperable clinical data. Yet most FHIR-enabled research to date has focused on standardizing and sharing data resources rather than integrating analytics directly with them—leaving a significant gap between data interoperability and actionable insight.
To address this gap, this study proposes a two-layer architectural framework that combines Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) with Fast Healthcare Interoperability Resources (FHIR) to enable native, FHIR-driven healthcare analytics. The data layer normalizes a broad range of FHIR resources into standard or use-case-specific representations, while the compute layer applies graph-based and generative analytics methods directly to these datasets. Within this framework, inference engines can be built simply by assembling clinical datasets conformant to the target analytics task, while a companion quality-assessment module evaluates the safety and accuracy of generated outputs, and generative models can synthesize new, task-relevant outputs as needed.
As a proof of concept, the framework is applied to a focal task—clinical decision support for given patient scenarios—using a GCN-based inference engine paired with a quality-assessment module to evaluate decision accuracy. The results demonstrate the framework's potential to bridge the gap between healthcare data interoperability and modern analytics, an area that remains comparatively underdeveloped relative to other domains.
References
1. Choi, E., Xu, Z., Li, Y., Dusenberry, M. W., Flores, G., Xue, E., & Dai, A. M. (2020). Learning the graphical structure of electronic health records with graph convolutional network. In Proceedings of the 2020 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
2. Wang, X., Choi, E., Xu, Z., & Wang, J. (2020). Graph-based deep learning for medical diagnosis and analysis: Past, present and future. IEEE Journal of Biomedical and Health Informatics, 24(12), 3447–3460.
3. Mangalampalli, B. M., Bandi, V. D. V. K., Kolla, S. K., & Kumar, M. V. K. (2025). Towards Self-Evolving Healthcare Intelligence: Integrating Advanced Learning Systems with Real-Time Clinical Data Pipelines. Cultura: International Journal of Philosophy of Culture and Axiology, 22(12s), 464-486.
4. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24.
5. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57–81.
6. Mattaparthi, R. (2025). GenAI-Augmented Diagnostic Reasoning for Diesel Engine Fault Triage: A Large Language Model Framework for Technician Decision Support at Scale. Journal of Material Sciences & Manufacturing Research, 6(12), 1.
7. Choi, E., Xiao, C., Stewart, W. F., & Sun, J. (2020). MiME: Multilevel medical embedding for 2D and 3D medical data. In Proceedings of the 2020 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
8. Zhang, Y., Chen, R., Tang, J., Stewart, W. F., & Sun, J. (2020). LEAP: Learning to prescribe effective and safe treatment combinations for multimorbidity. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 142–149.
9. Kolla, S. H., & Mangala, N. (2025). DESIGNING AUTONOMOUS LLM AGENT FRAMEWORKS USING GEN AI PIPELINES TO ENHANCE CUSTOMER SERVICE MANAGEMENT AND KNOWLEDGE WORKFLOWS. Lex Localis-Journal of Local Self-Government, 23, 9719-9733.
10. Choi, E., Bahadori, M. T., Song, L., Stewart, W. F., & Sun, J. (2021). GRAM: Graph-based attention model for healthcare representation learning. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
11. Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G. (2021). BEHRT: Transformer for electronic health records. Scientific Reports, 10, 7155.
12. Loganathan, R. (2025). AGENTIC AI FRAMEWORKS FOR AUTONOMOUS RISK DETECTION AND COMPLIANCE REMEDIATION IN ENTERPRISE DATA CENTER OPERATIONS. Lex Localis-Journal of Local Self-Government, 23 (S6), 9672–9697.
13. Shang, J., Xiao, C., Ma, T., Li, H., & Sun, J. (2021). Pre-training of graph augmented transformers for medication recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 834–841.
14. Jin, W., Qu, M., Jin, X., & Ren, X. (2021). Rigorous self-supervised graph representation learning for predicting drug-drug interactions. Proceedings of the 30th ACM International Conference on Information and Knowledge Management.
15. Mangalampalli, B. M., Kolla, S. K., Bandi, V. D. V. K., Yandamuri, U. S., & Rani, P. S. (2025). Designing Intelligent Healthcare Ecosystems through Adaptive Data Integration and Autonomous Learning Systems. Vascular and Endovascular Review, 8(20s), 330-347.
16. Sun, T., Wang, C., & others. (2021). Graph neural networks for healthcare applications: A survey. IEEE Access, 9, 113457–113477.
17. Choi, E., & Sun, J. (2022). Learning representations from electronic health records using graph neural networks. Journal of Biomedical Informatics, 129, 104076.
18. Yang, C., Xiao, Y., Zhang, Y., Sun, Y., & Han, J. (2022). Heterogeneous network representation learning: A unified framework with survey and empirical analysis. IEEE Transactions on Knowledge and Data Engineering, 34(5), 2271–2285.
19. Mahadevan, S. (2025). DRIVING SUSTAINABILITY IN AUTO & MANUFACTURING SECTORS: THE ROLE OF CLOUD-BASED SERVERLESS AUTOMATION FOR ERP SYSTEMS. International Journal of Applied Mathematics, 38(7s), 1813-1825.
20. Wu, L., Lin, H., Tan, C., Gao, Z., & Li, S. Z. (2022). Self-supervised learning on graphs: Deep insights and new directions. IEEE Transactions on Knowledge and Data Engineering, 35(10), 10139–10154.
21. Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A. K., Cole-Lewis, H., Pfohl, S., Payne, P., Seneviratne, M., Gamble, P., Kelly, C., Schärli, N., Chowdhery, A., Mansfield, P., Demner-Fushman, D., Agrawal, M., ... Natarajan, V. (2023). Large language models encode clinical knowledge. Nature, 620, 172–180.
22. Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, J., Pfohl, S., Cole-Lewis, H., Neal, D., & others. (2023). Towards expert-level medical question answering with large language models. arXiv.
23. Kolla, S. K., & Reddy, V. A. R. (2024). Evaluating Cloud-Native vs. Hybrid Architectures for Health Benefit Administration Systems. International Journal of Medical Toxicology and Legal Medicine, 27(5), 1042-1053.
24. Nori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023). Capabilities of GPT-4 on medical challenge problems. arXiv.
25. Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616, 259–265.
26. Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., Zhang, Y., & others. (2023). A large language model for electronic health records. npj Digital Medicine, 6, 149.
27. Mangalampalli, B. M., & Kolla, S. K. (2025). Large Language Models for Automated Healthcare Data Dictionary Generation and Maintenance. Vascular and Endovascular Review, 8(20s), 363-375.
28. Lehman, E., Jain, S., Paguet, C., & others. (2023). Do we still need clinical language models? Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.
29. Yang, C., Liu, Z., Zhang, C., & others. (2023). GNN-based representation learning for clinical risk prediction from electronic health records. Artificial Intelligence in Medicine, 139, 102528.
30. Boll, H. O., Amirahmadi, A., Ghazani, M. M., Morais, W. O. de, Freitas, E. P. de, Soliman, A., Etminani, F., Byttner, S., & Recamonde-Mendoza, M. (2024). Graph neural networks for clinical risk prediction based on electronic health records: A survey. Journal of Biomedical Informatics, 151, 104616.
31. Mangala, N., & Kolla, S. H. (2025). Real-Time Feature Engineering for Streaming AI Workloads Using PySpark and Azure Event Hubs. International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management, 1(6), 93-105.
32. Paul, S. G., Saha, A., Hasan, M. Z., Noori, S. R. H., & Moustafa, A. (2024). A systematic review of graph neural network in healthcare-based applications: Recent advances, trends, and future directions. IEEE Access, 12, 15145–15170.
33. Wang, D., & Zhang, S. (2024). Large language models in medical and healthcare fields: Applications, advances, and challenges. Artificial Intelligence Review, 57, 299.
34. Nerella, S., Bandyopadhyay, S., Zhang, J., Contreras, M., Siegel, S., Bumin, A., Silva, B., Sena, J., Shickel, B., Bihorac, A., Khezeli, K., & Rashidi, P. (2024). Transformers and large language models in healthcare: A review. Artificial Intelligence in Medicine, 154, 102900.
35. Wang, Y., Zhao, Y., Zhang, M., & others. (2024). Large language models in medicine: A systematic review. Journal of Medical Internet Research, 26, e22769.
36. Yim, D., Khuntia, J., Parameswaran, V., & Meyers, A. (2024). Preliminary evidence of the use of generative AI in health care clinical services: Systematic narrative review. JMIR Medical Informatics, 12, e52073.
37. Mahadevan, S. (2024). Intelligent Serverless Process Automation for Scalable Cloud Operations and Dynamic Resource Optimization. Journal of Computational Analysis and Applications (JoCAAA), 33(06), 4207-4221.
38. Omar, M., Nadkarni, G. N., Klang, E., & Glicksberg, B. S. (2024). Large language models in medicine: A review of current clinical trials across healthcare applications. PLOS Digital Health, 3(11), e0000662.
39. Xiao, H., Zhou, F., Liu, X., Liu, T., Li, Z., Liu, X., & Huang, X. (2025). A comprehensive survey of large language models and multimodal large language models in medicine. Information Fusion, 117, 102888.
40. Kather, J. N., Calderaro, J., & others. (2025). Graph artificial intelligence in medicine. Annual Review of Biomedical Data Science.
Additional Files
Published
Issue
Section
License
Copyright (c) 2026 Sophia Martinez (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The American Online Journal of Science and Engineering (AOJSE) is an open-access journal, and all published articles are made freely available to readers worldwide under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license governs the use, sharing, adaptation, and distribution of all content published in AOJSE and is designed to promote the broadest possible dissemination and reuse of scholarly research.
Creative Commons Attribution 4.0 International License (CC BY 4.0)
Under the Creative Commons Attribution 4.0 International License, any user is free to share, copy, and redistribute the published material in any medium or format, and to adapt, remix, transform, and build upon the material for any purpose, including commercial use, provided that appropriate credit is given to the original authors and source. The license cannot be revoked as long as the terms are followed.
When using or redistributing content published in AOJSE, users must provide proper attribution by citing the original authors' names, the article title, the journal name (AOJSE), the volume and issue number, the year of publication, and the DOI or URL of the article. Users must also indicate if any changes were made to the original work. Attribution must be provided in a reasonable manner but must not suggest that the authors or AOJSE endorse the user or their use of the material.
Author Rights and Copyright Retention
AOJSE respects and upholds the intellectual property rights of all contributing authors. Authors who publish in AOJSE retain full copyright over their work. By submitting a manuscript to AOJSE, authors grant the journal a non-exclusive, worldwide, royalty-free license to publish, reproduce, distribute, publicly display, and archive the article in print and electronic formats. This license allows AOJSE to make the article freely accessible to readers globally while ensuring that the authors remain the rightful owners of their work.
Authors are permitted to deposit their published articles in institutional repositories, personal websites, academic networking platforms, and preprint servers, provided that the original publication in AOJSE is acknowledged and properly cited. Authors may also reuse their published content in subsequent works, presentations, teaching materials, and grant applications without requiring prior permission from the journal.
Third-Party Content
Authors are solely responsible for obtaining written permission to reproduce any third-party material, including figures, tables, images, data, or excerpts from other publications, included in their manuscript. Evidence of such permissions must be provided to the editorial office upon request. AOJSE does not assume any responsibility for copyright infringement arising from the unauthorized inclusion of third-party content in published articles.
Permitted Uses
Under the CC BY 4.0 license, the following uses of AOJSE published content are freely permitted without prior written permission from the journal or authors, provided that proper attribution is given. Users may read, download, print, and distribute articles for personal, educational, or research purposes. Researchers may reuse data, figures, and findings for meta-analyses, systematic reviews, and secondary research. Educators may incorporate published articles into course materials, syllabi, and academic presentations. Journalists, policymakers, and practitioners may reference and cite published findings for professional and public interest purposes.
Prohibited Uses
While the CC BY 4.0 license is broad and permissive, users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Users may not misrepresent the original authorship of published content or imply endorsement by the original authors or AOJSE without explicit consent. Plagiarism, data fabrication, and any form of academic misconduct in the reuse of published material are strictly prohibited and are a violation of publication ethics standards.
Institutional and Repository Deposit Policy
AOJSE fully supports self-archiving and green open access. Authors are encouraged to deposit the final published version of their article, also known as the version of record, in institutional repositories, subject repositories, and open-access databases immediately upon publication. There is no embargo period. Authors should always link back to the original article on the AOJSE website and include the full citation and DOI when depositing their work in any repository.
Digital Preservation and Archiving
AOJSE is committed to the long-term digital preservation of all published content to ensure its permanent availability and accessibility. The journal utilizes the Open Journal Systems (OJS) platform, which supports internationally recognized digital archiving standards. AOJSE encourages participation in archiving networks such as LOCKSS (Lots of Copies Keep Stuff Safe) and CLOCKSS (Controlled LOCKSS) to safeguard published articles against data loss and ensure continued access for future generations of researchers and readers.
Disclaimer
The views, opinions, findings, and conclusions expressed in articles published in AOJSE are solely those of the authors and do not necessarily reflect the official position, policy, or views of the journal, its editorial board, or its affiliated organizations. AOJSE makes every effort to ensure the accuracy and integrity of published content but does not accept legal responsibility for any errors, omissions, or claims arising from the use of information published in the journal.
Contact for Licensing Inquiries
For any questions regarding the licensing terms, copyright, or permitted use of content published in AOJSE, please contact the editorial office through the journal website at https://aojse.org. The editorial team will be happy to assist authors, readers, and institutions with any licensing-related queries.
