Graph-Based Adaptive Coding and Live Risk Insights
Keywords:
Adaptive Coding, CA-CCDA, Clinical Data Interoperability, Clinical Decision Support, Clinical Graphs, Electronic Health Records (EHRs), HL7 Standards, Interoperable Healthcare Systems, Patient Risk Assessment, Predictive Healthcare Analytics, Quality Indicators, Real-Time Clinical Analytics, Risk Stratification, Semantic Healthcare Data.Abstract
As technology increasingly enables the storage of massive volumes of supporting data, attention is turning toward developing solutions that put this data to novel use—particularly for predictive analysis. This paper presents a solution built on an interoperable clinical graph framework capable of generating real-time patient risk insights. Several adaptive coding engines, operating on a shared adaptive coding principle, produce distinct models tailored to different needs, including care, education, and research. Current work focuses on cards representing risk and quality indicators derived from CA-CCDA files; these indicator cards provide a real-time summary of a patient’s risk across various adverse outcomes. Once available, a clinical graph representing patients—along with their diagnoses, procedures, medications, and problems—will be integrated into the broader interoperable clinical graph framework. We share progress toward this integration and outline future plans.
Clinical graphs also play an essential role at scale. Complete CA-CCDA resources from multiple institutions can be used to generate content representations in the form of easily interpretable clinical graphs, enabling rapid overview of large patient cohorts.
A clinical graph is a succinct representation of a patient’s CA-CCDA health summary, capturing major historical clinical events—diagnoses, procedures, medications, and care—organized within key HL7 domains. Graph construction is driven primarily by formal metadata extracted directly from CA-CCDA files. This metadata description of relevant HL7 domains can periodically generate group-specific clinical graph enrichments, functioning as data packets for secondary purposes such as publishing, education, clinical research, and training—domains that similarly require rapid overview across large information collections.c
References
1. Cao, B., Wang, Z., & Zhu, E. (2024). Graph neural networks of improved code attribute graph for software vulnerability detection. Microelectronics & Computer, 41(1), 74–82.
2. Chen, J., Yin, Y., Cai, S., Wang, W., Wang, S., & Chen, J. (2024). iGnnVD: A novel software vulnerability detection model based on integrated graph neural networks. Science of Computer Programming, 238, Article 103156.
3. Ghaffarian, S. M., & Shahriari, H. R. (2021). Neural software vulnerability analysis using rich intermediate graph representations of programs. Information Sciences, 553, 189–207.
4. Manukonda, K. R. R. (2024). Chapter-5 Enhancing Test Automation Efficiency Through AI-driven Reporting: An Empirical Study on Defect Detection and Resolution Time. ADVANCES IN, 119, 107.
5. Hin, D., Kan, A., Chen, H., & Babar, M. A. (2022). LineVD: Statement-level vulnerability detection using graph neural networks. arXiv preprint.
6. Kosasih, E. E., Margaroli, F., Gelli, S., Aziz, A., Wildgoose, N., & Brintrup, A. (2024). Towards knowledge graph reasoning for supply chain risk management using graph neural networks. International Journal of Production Research, 62(15), 5596–5612.
7. Liu, H., Jiang, S., Qi, X., Qu, Y., Li, H., Li, T., Guo, C., & Guo, S. (2024). Detect software vulnerabilities with weight biases via graph neural networks. Expert Systems with Applications, 238, Article 121764.
8. KOLLA, S. K. (2024). DEEP LEARNING AND CLINICAL NLP FRAMEWORK FOR AUTOMATED KNOWLEDGE EXTRACTION FROM MULTIMODAL HEALTH . TPM – Testing, Psychometrics, Methodology in Applied Psychology, 31(S2), 134–140. Retrieved from https://tpmap.org/submission/index.php/tpm/article/view/4748
9. McLaughlin, C., & Lu, Y. (2024). Multi-class vulnerability prediction using value flow and graph neural networks. Neural Computing and Applications, 36, 15869–15891.
10. Wang, H., Ye, G., Tang, Z., Tan, S. H., Huang, S., Fang, D., & others. (2021). Combining graph-based learning with automated data collection for code vulnerability detection. IEEE Transactions on Information Forensics and Security, 16, 1943–1958.
11. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.
12. Wang, S., Huang, C., & Yu, D. (2023). VulGraB: Graph-embedding-based code vulnerability detection with bi-directional gated graph neural network. Software: Practice and Experience, 53(8), 1631–1658.
13. Wu, B., & Zou, F. (2022). Code vulnerability detection based on deep sequence and graph models: A survey. Security and Communication Networks, 2022, Article 1176898.
14. Yang, J., Ruan, O., & Zhang, J. (2024). Tensor-based gated graph neural network for automatic vulnerability detection in source code. Software Testing, Verification and Reliability, 34(2), Article e1867.
15. Zou, D., Wang, S., Xu, S., Ou, X., Jin, H., & Wang, S. (2021). μVulDeePecker: A deep learning-based system for multiclass vulnerability detection. IEEE Transactions on Dependable and Secure Computing, 18(5), 2224–2236.
16. Rohit Gorle. (2024). Deep Learning Approaches for Threat Detection in Multi-Cloud Environments. Journal of Computational Analysis and Applications (JoCAAA), 33(07), 3857–3864. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5879
17. Sheth, A., & Sinfield, J. V. (2024). Advancing the complex adaptive systems approach to enterprise risk management with quantified risk networks (QRNs). Scientific Reports, 14, Article 22312.
18. Sun, et al. (2024). A graph embedding-based approach for automatic cyber-physical power system risk assessment to prevent and mitigate threats at scale. IET Cyber-Physical Systems: Theory & Applications, 9(4), 435–453.
19. Zhang, et al. (2023). A novel vulnerability severity assessment method for source code based on a graph neural network. Information and Software Technology.
20. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
21. (2023). Cross-domain vulnerability detection using graph embedding and domain adaptation. Computers & Security, 125, Article 103017.
22. (2023). Improving vulnerability detection with hybrid code graph representation. In Proceedings of the 30th Asia-Pacific Software Engineering Conference (APSEC).
23. (2024). A compact vulnerability knowledge graph for risk assessment. ACM Transactions on Knowledge Discovery from Data.
24. (2024). Graph confident learning for software vulnerability detection. Engineering Applications of Artificial Intelligence.
25. Mashetty, S. (2024). Leveraging Deep Learning, Neural Networks, and Data Engineering for Intelligent Mortgage Loan Validation: A Data-Driven Approach to Automating Borrower Income, Employment, and Asset Verification. Neural Networks, and Data Engineering for Intelligent Mortgage Loan Validation: A Data-Driven Approach to Automating Borrower Income, Employment, and Asset Verification (December 21, 2024).
26. (2024). DSHGT: Dual-supervisors heterogeneous graph transformer—A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities. ACM Transactions on Software Engineering and Methodology, 33(8), Article 202.
27. Wang, W., Song, J., Xu, G., Li, Y., Wang, H., & Su, C. (2021). ContractWard: Automated vulnerability detection models for Ethereum smart contracts. IEEE Transactions on Network Science and Engineering, 8(2), 1133–1144.
28. Mahadevan, S., Desani, N. R., Yadav, P. S., Joseph, T., & Malcom, A. (2024). FutureTech Innovations. Cari Journals USA LLC.
29. (2021). Graph neural networks for program analysis and software vulnerability detection. IEEE Transactions on Information Forensics and Security.
30. (2022). Graph neural network approaches for software defect prediction and vulnerability analysis. Journal of Systems and Software.
31. (2022). Deep learning-based software vulnerability detection using code representation and graph learning. Applied Sciences.
32. (2022). Graph-based representation learning for source code vulnerability detection. Computers & Security.
33. Movva, V. R. (2024). Cloud-Native Platform Engineering for High-Scale Retail Systems: Designing Resilient Microservices and Intelligent Infrastructure. Available at SSRN 7449480.
34. (2023). Learning code representations for software vulnerability prediction using graph neural networks. Information and Software Technology.
35. (2023). Graph neural networks for software security: Vulnerability detection and analysis. Journal of Systems and Software.
36. (2023). Knowledge graph reasoning for risk identification and prediction in complex systems. Expert Systems with Applications.
37. 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.
38. (2023). Dynamic graph representation learning for evolving network risk analysis. Knowledge-Based Systems.
39. (2023). Graph-based anomaly detection for cybersecurity and network threat intelligence. Computers & Security.
40. Manukonda, K. R. R., Swamy, H., Sankar, R., & CM, C. (2024, August). Application of Explicit Group Sparse Projection in Optimizing 5G Network Resource Allocation and Topology Design. In 2024 4th Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-6). IEEE.
41. (2024). Graph learning for software vulnerability detection and severity prediction. Neural Computing and Applications.
42. (2024). Graph representation learning for cyber risk assessment in interconnected systems. Computers & Security.
43. Mahadevan, S. (2024). Empowering Manufacturing: Generative AI Revolutionizes ERP Application. Int. J. Innov. Sci. Res, 9, 593-595.
44. (2024). Knowledge graph-based risk identification and prediction for complex industrial systems. Journal of Hydrology, 635, Article 131155.
45. (2024). Graph-based risk propagation and intelligent threat assessment in cyber-physical systems. IET Cyber-Physical Systems: Theory & Applications.
46. Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
47. (2024). Adaptive graph learning for software security and vulnerability prediction. Expert Systems with Applications.
Additional Files
Published
Issue
Section
License
Copyright (c) 2025 Srinivas Kalyan, Alexander Petrov (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.
