Graph Neural Networks for HCC Risk Adjustment and Interoperability
Abstract
Hepatocellular carcinoma (HCC) is the leading cause of cancer-related death among patients with cirrhosis. Accurate estimation of prognostic risk is crucial for patient management and clinical trial design. Existing models for risk prediction after diagnosis of HCC are affected by high degrees of uncertainty, particularly in children and older adults. Additionally, while substantial heterogeneity exists in clinical outcomes, risk-prediction models do not officially incorporate it. One potential way to address these issues is through outcome predictive modeling techniques such as survival analysis with random-effects. However, existing random-effects models for HCC have only been built with a small number of variables, limiting translation into clinical practice . Two data sources, the National Inpatient Sample and the United Network for Organ Sharing dataset, were therefore merged
Data quality and heterogeneity remain major challenges and limitations of these datasets. As in many domains, hospital diagnosis codes are the only data readily available for a substantial share of patients. Furthermore, different organizations use different combinations of variables for clinical assessment and prediction. The use of data from multiple sources adds complexity because of differences in coding and outcomes between organizations. Model performance will not be meaningfully improved by simply using more variables. Standard summary statistics on prediction accuracy are sensitive to differences in data quality and coding practices. The utility of existing data therefore extends beyond just risk prediction to risk adjustment, that is, ensuring fair comparisons across different demographics. An unsupervised graph representation of the data that jointly identified high-quality codes and dislike-like patterns was applied
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
13244-13255 |
Published |
December 12, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Triveni Kolla (%2024). Graph Neural Networks for HCC Risk Adjustment and Interoperability. International Journal of Science, Research and Technology , Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 13244-13255. https://doi.org/10.15662/IJSRAT.2024.0706011 |
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[33] Pamisetty, A. (2024). Leveraging Agentic AI and Cloud Infrastructure for Predictive Logistics in National Food Supply Chains. Available at SSRN 5262994.
[34] Deep Learning-Driven Optimization of ISO 20022 Protocol Stacks for Secure Cross-Border Messaging. (2024). MSW Management Journal, 34(2), 1545-1554.
[35] Pamisetty, A. (2024). Leveraging Big Data Engineering for Predictive Analytics in Wholesale Product Logistics. Available at SSRN 5231473.
[36] Kolla, S. K. (2023). Explainable AI and ML Models for Transparent Clinical Decision Support. Journal for ReAttach Therapy and Developmental Diversities, 6, 2444-2460.
[37] Nagabhyru, K. C. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898-5910.
[38] Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
[39] Sheelam, G. K. (2024). Towards autonomic wireless systems: integrating agentic AI with advanced semiconductor technologies in telecommunications. Am. Online J. Sci. Eng., 3(4), 234-256.
[40] Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology (IJSRMT).
[41] Amistapuram, K. (2024). Federated Learning for Cross-Carrier Insurance Fraud Detection: Secure Multi-Institutional Collaboration. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 6727-6738.
[42] Reddy Segireddy, A. (2024). Federated Cloud Approaches for Multi-Regional Payment Messaging Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(2), 442-450.
[43] 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.
[44] Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
[45] 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.
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[47] Mahesh Recharla, “Integrated Genomic and Neurobiological Pathway Mapping for Early Detection of Alzheimer’s Disease,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2023.12122.
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[51] Mangalampalli, B. M. (2024). AI-Enhanced Data Governance: Automating Compliance In Healthcare Analytics Platforms. The Review of Diabetic Studies, 191-204.
[52] Meda, R. (2024). Enhancing Paint Formula Innovation Using Generative AI and Historical Data Analytics. American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN, 3067-4190.
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[54] Kolla, S. K. (2023). Big Data–Driven Machine Learning Frameworks for Clinical Risk Prediction. International Journal of Medical Toxicology and Legal Medicine, 26(3), 44-59.
[55] Pamisetty, V. (2024). Transforming taxation systems through predictive analytics and AI-driven compliance monitoring tools. Am Data Sci J Adv Comput, 3, 55-68.
[56] Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
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[58] Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Available at SSRN 5532601.
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[60] Gottimukkala, V. R. R. (2023). Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 633-652.
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[62] Kolla, S. H. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10, 495-506.
[63] Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30 (4), 1011–1027.
[64] Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research , 101–122. https://doi.org/10.63278/jicrcr.vi.3738.
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[66] Meda, R. (2024). Agentic AI in Multi-Tiered Paint Supply Chains: A Case Study on Efficiency and Responsiveness. Journal of Compu-tational Analysis and Applications (JoCAAA), 33(08), 3994-4015.
[2] 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.
[3] Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
[4] Kolla, S. H. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications, 31(4).
[5] Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
[6] 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.
[7] 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.
[8] Pamisetty, V., & Amistapuram, K. Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning.
[9] Singireddy, J. (2023). Finance 4.0: Predictive analytics for financial risk management using AI. European Journal of Analytics and Artificial Intelligence (EJAAI) p-ISSN, 3050-9556.
[10] 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. https://doi.org/10.31586/jaibd.2021.1361
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[12] Nandan, B. P. (2024). Semiconductor Process Innovation: Leveraging Big Data for Real-Time Decision-Making. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 4038-4053.
[13] Singireddy, J. (2024). Deep Learning Architectures for Automated Fraud Detection in Payroll and Financial Management Services: Towards Safer Small Business Transactions. Journal of Artificial Intelligence and Big Data Disciplines, 1(1), 75-85.
[14] Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology
[15] Sheelam, G. K. (2024). Deep Learning-Based Protocol Stack Optimization in High-Density 5G Environments. European Advanced Journal for Science & Engineering (EAJSE)-p-ISSN, 3050-9696.
[16] Pamisetty, A., Adusupalli, B., Mashetty, S., & Singreddy, S. (2024). Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management. Sneha, Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management (December 05, 2024).
[17] Kolla, S. H. (2024). RETRIEVAL-AUGMENTED GENERATION WITH SMALL LLMS FOR KNOWLEDGE-DRIVEN DECISION AUTOMATION IN ENTERPRISE SERVICE PLATFORMS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 476-486.
[18] Singreddy, S. (2024). Applying deep learning to mobile home and flood insurance risk evaluation. Available at SSRN 5238946.
[19] Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
[20] Inala, R. (2022). Engineering Data Products for Investment Analytics: The Role of Product Master Data and Scalable Big Data Solutions. International Journal of Scientific Research and Modern Technology, 155-171.
[21] Singireddy, S. (2024). The Integration of AI and Machine Learning in Transforming Underwriting and Risk Assessment Across Personal and Commercial Insurance Lines. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 3966-3991.
[22] Kummari, D. N. (2023). AI-powered demand forecasting for automotive components: A multi-supplier data fusion approach. European Advanced Journal for Emerging Technologies (EAJET)-p-ISSN, 3050-9734.
[23] Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement lanning. Educational Administration: Theory and Practice, 29 (4), 5493–5505
[24] Nandan, B. P. (2024). Revolutionizing Semiconductor Chip Design through Generative AI and Reinforcement Learning: A Novel Approach to Mask Patterning and Resolution Enhancement. International Journal of Medical Toxicology and Legal Medicine, 27(5), 759-772.
[25] Kolla, T. (2024). AI-Powered Data Catalog Systems For Healthcare Data Discovery And Governance. South Eastern European Journal of Public Health, 2296–2311. https://doi.org/10.70135/seejph.vi.7077
[26] Inala, R., & Somu, B. (2024). Agentic AI in Retail Banking: Redefining Customer Service and Financial Decision-Making. Journal of Artificial Intelligence and Big Data Disciplines, 1(1).
[27] Recharla, M. (2024). Advances in Therapeutic Strategies for Alzheimer’s Disease: Bridging Basic Research and Clinical Applications. American Online Journal of Science and Engineering (AOJSE)(ISSN: 3067-1140), 2(1).
[28] Segireddy, A. R. (2024). Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications. Journal of Computational Analysis and Applications, 33(8).
[29] Amistapuram, K. (2024). Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning. Available at SSRN 6143426
[30] Kolla, S. K. (2024). Federated Machine Learning On Big Healthcare Data For Privacy-Preserving Analytics. The Review of Diabetic Studies, 175-190.
[31] Singireddy, J. (2024). AI-Driven Payroll Systems: Ensuring Compliance and Reducing Human Error. American Data Science Journal for Advanced Computations (ADSJAC) ISSN, 3067-4166.
[32] 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.
[33] Pamisetty, A. (2024). Leveraging Agentic AI and Cloud Infrastructure for Predictive Logistics in National Food Supply Chains. Available at SSRN 5262994.
[34] Deep Learning-Driven Optimization of ISO 20022 Protocol Stacks for Secure Cross-Border Messaging. (2024). MSW Management Journal, 34(2), 1545-1554.
[35] Pamisetty, A. (2024). Leveraging Big Data Engineering for Predictive Analytics in Wholesale Product Logistics. Available at SSRN 5231473.
[36] Kolla, S. K. (2023). Explainable AI and ML Models for Transparent Clinical Decision Support. Journal for ReAttach Therapy and Developmental Diversities, 6, 2444-2460.
[37] Nagabhyru, K. C. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898-5910.
[38] Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
[39] Sheelam, G. K. (2024). Towards autonomic wireless systems: integrating agentic AI with advanced semiconductor technologies in telecommunications. Am. Online J. Sci. Eng., 3(4), 234-256.
[40] Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology (IJSRMT).
[41] Amistapuram, K. (2024). Federated Learning for Cross-Carrier Insurance Fraud Detection: Secure Multi-Institutional Collaboration. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 6727-6738.
[42] Reddy Segireddy, A. (2024). Federated Cloud Approaches for Multi-Regional Payment Messaging Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(2), 442-450.
[43] 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.
[44] Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
[45] 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.
[46] Pamisetty, V. (2024). AI-Driven Decision Support for Taxation and Unclaimed Property Management: Enhancing Efficiency through Big Data and Cloud Integration. Available at SSRN 5250776.
[47] Mahesh Recharla, “Integrated Genomic and Neurobiological Pathway Mapping for Early Detection of Alzheimer’s Disease,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2023.12122.
[48] Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN 5697663. [91]Meda, R. (2024). Predictive Maintenance of Spray Equipment Using Machine Learning in Paint Application Services. European Data Science Journal (EDSJ) p-ISSN, 3050-9572.
[49] Sheelam, G. K., & Koppolu, H. K. R. (2024). From Transistors to Intelligence: Semiconductor Architectures Empowering Agentic AI in 5G and Beyond. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 4518-4537.
[50] Bandi, V. D. V. K. (2024). Automated Feature Engineering Systems in Large-Scale Healthcare Data Environments. Journal of Neonatal Surgery, 13.
[51] Mangalampalli, B. M. (2024). AI-Enhanced Data Governance: Automating Compliance In Healthcare Analytics Platforms. The Review of Diabetic Studies, 191-204.
[52] Meda, R. (2024). Enhancing Paint Formula Innovation Using Generative AI and Historical Data Analytics. American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN, 3067-4190.
[53] Bandi, V. D. V. K. (2024). AI-Driven Predictive Risk Modeling Architectures for Financial Systems. International Journal Of Finance, 37(3), 54-7.
[54] Kolla, S. K. (2023). Big Data–Driven Machine Learning Frameworks for Clinical Risk Prediction. International Journal of Medical Toxicology and Legal Medicine, 26(3), 44-59.
[55] Pamisetty, V. (2024). Transforming taxation systems through predictive analytics and AI-driven compliance monitoring tools. Am Data Sci J Adv Comput, 3, 55-68.
[56] Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
[57] Nagabhyru, K. C. (2024). Data Engineering in the Age of Large Language Models: Transforming Data Access, Curation, and Enterprise Interpretation. Computer Fraud and Security.
[58] Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Available at SSRN 5532601.
[59] Meda, R. (2023). Data Engineering Architectures for Scalable AI in Paint Manufacturing Operations. European data science journal.
[60] Gottimukkala, V. R. R. (2023). Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 633-652.
[61] Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
[62] Kolla, S. H. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10, 495-506.
[63] Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30 (4), 1011–1027.
[64] Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research , 101–122. https://doi.org/10.63278/jicrcr.vi.3738.
[65] Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
[66] Meda, R. (2024). Agentic AI in Multi-Tiered Paint Supply Chains: A Case Study on Efficiency and Responsiveness. Journal of Compu-tational Analysis and Applications (JoCAAA), 33(08), 3994-4015.