Articles
Clinical Knowledge Intelligence through Deep Learning and Natural Language Understanding in Healthcare Platforms
Abstract
To support the aspiration of closing the gap between academia and clinical practice, recent years have witnessed the emergence of a range of healthcare platforms that argue the ability to extract, integrate, mine and deliver actionable clinical intelligence from the data deposited during healthcare provision. Clinical Knowledge Intelligence represents an increasingly mature thread of research in which a range of Deep Learning and Natural Language Processing methods operate in combination to extract knowledge from clinical documents, construct a Knowledge Graph which links together the entities identified and delivered as answers to questions related to the clinical narrative of individual patients.
The aspirations of Clinical Knowledge Intelligence platforms surpass the boundaries of classical statistical methods stemming from Machine Learning. Efforts have been dedicated to the academic community and progressively high-profile publications from industry players have cashed in on the success of Transformer-based models for Clinical Language Understanding, an area where EHRs and clinical notes have been exploited as a primary source. Within healthcare platforms, the emphasis lies more in the orchestration of Natural Language Understanding blocks that consume and organise knowledge with respect to a Knowledge Graph than in the performance of any single task. Within this vein, four tasks have received attention: Named Entity Recognition, Relation Extraction, Temporal Reasoning and Negation/Uncertainty detection. Supporting the underpinning clinical ontology, code mapping in the context of the most widely employed coding systems–ICD, CPT and SNOMED–is also a focus of research, especially to bolster interoperability capabilities
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 7 No. 3 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
14077-14088 |
Published |
May 5, 2024 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Sasi Kumar Kolla (2024). Clinical Knowledge Intelligence through Deep Learning and Natural Language Understanding in Healthcare Platforms. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 3 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 14077-14088. https://doi.org/10.15662/IJAESIT.2024.0703005 |
References
[1] Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.
[2] 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.
[3] LangChain Inc. (2024). Agent architectures and autonomous workflows with large language models. Technical Report.
[4] Alsentzer, E., Murphy, J. R., Boag, W., Weng, W. H., Jin, D., Naumann, T., & McDermott, M. (2019). Publicly available clinical BERT embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop, 72–78.
[5] Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
[6] Huang, K., Altosaar, J., & Ranganath, R. (2020). ClinicalBERT: Modeling clinical notes and predicting hospital readmission. arXiv preprint arXiv:1904.05342.
[7] Rajesh Mattaparthi. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781–8799. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5482
[8] Rasmy, L., Xiang, Y., Xie, Z., Tao, C., & Zhi, D. (2021). Med-BERT: Pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digital Medicine, 4(1), 86.
[9] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W. T., Rocktäschel, T., Riedel, S., & Kiela, D. (2024). Retrieval-augmented generation for knowledge-intensive NLP tasks. Nature Machine Intelligence, 6(2), 89–101.
[10] 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.
[11] Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2021). Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare, 3(1), 1–23.
[12] Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology.
[13] Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., Payne, P., Seneviratne, M., Gamble, P., Kelly, C., Scharli, N., Chowdhery, A., Mansfield, P., & others. (2023). Large language models encode clinical knowledge. Nature, 620(7972), 172–180.
[14] Li, Y., Wang, S., Zhang, H., & Chen, X. (2024). Multi-agent large language model systems for enterprise automation. IEEE Access, 12, 104233–104249.
[15] 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.
[16] Liao, Q. V., Gruen, D., & Miller, S. (2024). Questioning the AI: Informing design practices for explainable autonomous systems. International Journal of Human–Computer Studies, 183, 103148.
[17] Kuhlmann, M., Satzger, G., & Fromm, H. (2024). Autonomous service operations using AI-driven orchestration. Information Systems Frontiers, 26(4), 1381–1397.
[18] Reddy, V. A. R. (2022). Designing Fault-Tolerant Data Ingestion Pipelines for High-Volume Healthcare Transactions. Frontiers in Health Informatics, 11, 861-889.
[19] Peffers, K., Tuunanen, T., & Niehaves, B. (2024). Digital innovation through intelligent process automation. Journal of Strategic Information Systems, 33(2), 101811.
[20] Agrawal, M., Hegselmann, S., Lang, H., Kim, Y., Fröhling, L., Schmitt, M., & others. (2023). Evaluating ChatGPT as a clinical decision support tool. NPJ Digital Medicine, 6(1), 205.
[21] 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.
[22] Nori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023). Capabilities of GPT-4 on medical challenge problems. arXiv preprint arXiv:2303.13375.
[23] Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
[24] Lehman, E., Jain, S., Pichotta, K., Goldberg, Y., & Wallace, B. C. (2023). HoloBench: Evaluating large language models in healthcare decision making. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 11234–11250.
[25] Venkata Akhilesh Ranga Reddy, Sasi Kumar Kolla. (2024). Infrastructure-As-Code Practices For Regulated Healthcare Cloud Environments. Metallurgical and Materials Engineering, 30(4), 1028–1042. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1984
[26] Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., & Wen, J. R. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223.
[27] 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.
[28] Kolla, S. K., & Mangalampalli, B. M. (2024). Edge-Based Deep Learning Systems for Point-of-Care Diagnostic Intelligence. Journal of Neonatal Surgery, 13(1), 2387–2399. Retrieved from https://www.jneonatalsurg.com/index.php/jns/article/view/10309
[29] Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Wang, C., & others. (2023). AutoGen: Enabling next-generation large language model applications. arXiv preprint arXiv:2308.08155.
[30] Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.
[31] OpenAI. (2023). GPT-4 technical report. arXiv preprint arXiv:2303.08774.
[32] Mialon, G., Dessì, R., Lomeli, M., Eysenbach, B., Yue, X., Chen, Z., Stavroulakis, G., Xu, P., Christodoulou, K., Stojnic, R., & others. (2023). Augmented language models: A survey. Transactions on Machine Learning Research, 2023, 1–50.
[33] 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.
[34] Xu, H., Zhao, S., Wang, X., & Li, J. (2024). Autonomous decision-making architectures for intelligent enterprise systems. Future Generation Computer Systems, 158, 325–339.
[35] Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2024). Trends in cloud AI and autonomous operations. IEEE Cloud Computing, 11(2), 18–29.
[36] Velangani Divya Vardhan Kumar Bandi. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011–1027. https://doi.org/10.63278/mme.v30i4.1938
[37] Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2022). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record analysis. IEEE Journal of Biomedical and Health Informatics, 26(2), 533–547.
[38] Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
[39] Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2022). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 23(1), bbab432.
[40] Kolla, T. (2024). Graph Neural Networks for HCC Risk Adjustment and Interoperability. International Journal of Science, Research and Technology, 7(6), 13244-13255.
[41] Solares, J. R. A., Raimondi, F. E., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., Nazarzadeh, M., & Salimi-Khorshidi, G. (2022). Deep learning for electronic health records: A comparative review of multiple deep neural architectures. Journal of Biomedical Informatics, 129, 104053.
[42] Minsky, M. (2024). Multi-agent intelligence and enterprise automation revisited. AI Magazine, 45(3), 55–67.
[43] Deep Learning-Driven Optimization of ISO 20022 Protocol Stacks for Secure Cross-Border Messaging. (2024). MSW Management Journal, 34(2), 1545-1554.
[44] Tinn, R., Cheng, H., Gu, Y., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2023). Fine-tuning large neural language models for biomedical natural language processing. Patterns, 4(4), 100729.
[45] 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
[46] Jin, Q., Dhingra, B., Liu, Z., Cohen, W. W., & Lu, X. (2021). PubMedQA: A dataset for biomedical research question answering. EMNLP 2021, 2567–2577.
[47] Mangalampalli, B. M. (2024). AI-Enhanced Data Governance: Automating Compliance In Healthcare Analytics Platforms. The Review Of DIABETIC STUDIES OPEN ACCESS.
[48] Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.
[49] Nagabhyru, K. C. (2024). Data Engineering in the Age of Large Language Models: Transforming Data Access, Curation, and Enterprise Interpretation. Computer Fraud and Security.
[50] Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731.
[51] Mangala, N. (2024). Leveraging Microsoft Fabric Lakehouse as an AI-Ready Data Platform for Enterprise Analytics. Journal of Information Systems Engineering and Management.
[52] Peddi, R. K. (2024). AI-Based Workforce Analytics for SLA Governance and Uptime Assurance in Data Centers. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8589-8601.
[53] Sasi Kumar Kolla, Venkata Akhilesh Ranga Reddy. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260. https://doi.org/10.70135/seejph.vi.7132
[54] Jensen, P. B., Jensen, L. J., & Brunak, S. (2012). Mining electronic health records: Towards better research applications and clinical care. Nature Reviews Genetics, 13(6), 395–405.
[55] Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2021). What clinicians want: Contextualizing explainable machine learning for clinical end use. Proceedings of Machine Learning Research, 126, 359–380.
[56] Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11), e745–e750.
[57] 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.
[58] Hitzler, P. (2021). Knowledge graphs and their role in explainable artificial intelligence. Frontiers in Big Data, 4, 708949.
[59] Pan, S., Hu, X., Long, G., Jiang, J., Yao, L., & Zhang, C. (2023). Large language models and knowledge graphs: Opportunities and challenges. arXiv preprint arXiv:2308.06374.
[60] Bodenreider, O. (2004). The Unified Medical Language System (UMLS): Integrating biomedical terminology. Nucleic Acids Research, 32(Database Issue), D267–D270.
[61] Rosemann, M. (2024). Process management in the era of autonomous enterprise systems. Business Process Management Journal, 30(4), 945–960.
[62] 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.
[63] Jarrahi, M. H., Lutz, C., Newlands, G., & Lehdonvirta, V. (2024). AI agents and the future of work. Nature Human Behaviour, 8(3), 412–420.
[64] Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
[65] Hevner, A., Storey, V. C., & Ramesh, V. (2024). Designing enterprise systems for autonomous decision intelligence. MIS Quarterly, 48(2), 573–598.
[66] Janssen, M., van der Voort, H., & Wahyudi, A. (2024). Factors influencing adoption of AI-driven autonomous governance systems. Government Information Quarterly, 41(2), 101942.
[67] Wang, Y., Li, Z., Xu, J., & Huang, K. (2024). Agentic workflow automation with large language models. Expert Systems with Applications, 247, 123456.
[68] Siva Hemanth Kolla, Raghunath Loganathan. (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. https://doi.org/10.63278/jicrcr.vi.3786
[69] Hossenfelder, S. (2024). Autonomous AI agents and organizational transformation. AI & Society, 39(2), 457–470.
[70] Bandi, V. D. V. K. (2024). AI-Driven Predictive Risk Modeling Architectures for Financial Systems. International Journal Of Finance, 37(3), 54-78.
[71] Donnelly, K. (2006). SNOMED-CT: The advanced terminology and coding system for eHealth. Studies in Health Technology and Informatics, 121, 279–290.
[72] Inala, R. (2023). Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 579-606.
[73] Schneider, J., Kuss, P., Abraham, R., & Meske, C. (2024). Governance of generative artificial intelligence for companies. arXiv Preprint arXiv:2403.10245.
[74] Harmon, P. (2024). Business process change in the age of generative AI and autonomous systems. Business Process Trends Journal, 22(1), 5–18.
[75] Reddy, V. A. R. (2024). Comparing Batch vs. Streaming Approaches in Healthcare Data Warehousing Environments. Journal of Neonatal Surgery, 13.
[2] 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.
[3] LangChain Inc. (2024). Agent architectures and autonomous workflows with large language models. Technical Report.
[4] Alsentzer, E., Murphy, J. R., Boag, W., Weng, W. H., Jin, D., Naumann, T., & McDermott, M. (2019). Publicly available clinical BERT embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop, 72–78.
[5] Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
[6] Huang, K., Altosaar, J., & Ranganath, R. (2020). ClinicalBERT: Modeling clinical notes and predicting hospital readmission. arXiv preprint arXiv:1904.05342.
[7] Rajesh Mattaparthi. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781–8799. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5482
[8] Rasmy, L., Xiang, Y., Xie, Z., Tao, C., & Zhi, D. (2021). Med-BERT: Pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digital Medicine, 4(1), 86.
[9] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W. T., Rocktäschel, T., Riedel, S., & Kiela, D. (2024). Retrieval-augmented generation for knowledge-intensive NLP tasks. Nature Machine Intelligence, 6(2), 89–101.
[10] 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.
[11] Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2021). Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare, 3(1), 1–23.
[12] Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology.
[13] Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., Payne, P., Seneviratne, M., Gamble, P., Kelly, C., Scharli, N., Chowdhery, A., Mansfield, P., & others. (2023). Large language models encode clinical knowledge. Nature, 620(7972), 172–180.
[14] Li, Y., Wang, S., Zhang, H., & Chen, X. (2024). Multi-agent large language model systems for enterprise automation. IEEE Access, 12, 104233–104249.
[15] 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.
[16] Liao, Q. V., Gruen, D., & Miller, S. (2024). Questioning the AI: Informing design practices for explainable autonomous systems. International Journal of Human–Computer Studies, 183, 103148.
[17] Kuhlmann, M., Satzger, G., & Fromm, H. (2024). Autonomous service operations using AI-driven orchestration. Information Systems Frontiers, 26(4), 1381–1397.
[18] Reddy, V. A. R. (2022). Designing Fault-Tolerant Data Ingestion Pipelines for High-Volume Healthcare Transactions. Frontiers in Health Informatics, 11, 861-889.
[19] Peffers, K., Tuunanen, T., & Niehaves, B. (2024). Digital innovation through intelligent process automation. Journal of Strategic Information Systems, 33(2), 101811.
[20] Agrawal, M., Hegselmann, S., Lang, H., Kim, Y., Fröhling, L., Schmitt, M., & others. (2023). Evaluating ChatGPT as a clinical decision support tool. NPJ Digital Medicine, 6(1), 205.
[21] 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.
[22] Nori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023). Capabilities of GPT-4 on medical challenge problems. arXiv preprint arXiv:2303.13375.
[23] Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
[24] Lehman, E., Jain, S., Pichotta, K., Goldberg, Y., & Wallace, B. C. (2023). HoloBench: Evaluating large language models in healthcare decision making. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 11234–11250.
[25] Venkata Akhilesh Ranga Reddy, Sasi Kumar Kolla. (2024). Infrastructure-As-Code Practices For Regulated Healthcare Cloud Environments. Metallurgical and Materials Engineering, 30(4), 1028–1042. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1984
[26] Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., & Wen, J. R. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223.
[27] 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.
[28] Kolla, S. K., & Mangalampalli, B. M. (2024). Edge-Based Deep Learning Systems for Point-of-Care Diagnostic Intelligence. Journal of Neonatal Surgery, 13(1), 2387–2399. Retrieved from https://www.jneonatalsurg.com/index.php/jns/article/view/10309
[29] Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Wang, C., & others. (2023). AutoGen: Enabling next-generation large language model applications. arXiv preprint arXiv:2308.08155.
[30] Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.
[31] OpenAI. (2023). GPT-4 technical report. arXiv preprint arXiv:2303.08774.
[32] Mialon, G., Dessì, R., Lomeli, M., Eysenbach, B., Yue, X., Chen, Z., Stavroulakis, G., Xu, P., Christodoulou, K., Stojnic, R., & others. (2023). Augmented language models: A survey. Transactions on Machine Learning Research, 2023, 1–50.
[33] 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.
[34] Xu, H., Zhao, S., Wang, X., & Li, J. (2024). Autonomous decision-making architectures for intelligent enterprise systems. Future Generation Computer Systems, 158, 325–339.
[35] Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2024). Trends in cloud AI and autonomous operations. IEEE Cloud Computing, 11(2), 18–29.
[36] Velangani Divya Vardhan Kumar Bandi. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011–1027. https://doi.org/10.63278/mme.v30i4.1938
[37] Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2022). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record analysis. IEEE Journal of Biomedical and Health Informatics, 26(2), 533–547.
[38] Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
[39] Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2022). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 23(1), bbab432.
[40] Kolla, T. (2024). Graph Neural Networks for HCC Risk Adjustment and Interoperability. International Journal of Science, Research and Technology, 7(6), 13244-13255.
[41] Solares, J. R. A., Raimondi, F. E., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., Nazarzadeh, M., & Salimi-Khorshidi, G. (2022). Deep learning for electronic health records: A comparative review of multiple deep neural architectures. Journal of Biomedical Informatics, 129, 104053.
[42] Minsky, M. (2024). Multi-agent intelligence and enterprise automation revisited. AI Magazine, 45(3), 55–67.
[43] Deep Learning-Driven Optimization of ISO 20022 Protocol Stacks for Secure Cross-Border Messaging. (2024). MSW Management Journal, 34(2), 1545-1554.
[44] Tinn, R., Cheng, H., Gu, Y., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2023). Fine-tuning large neural language models for biomedical natural language processing. Patterns, 4(4), 100729.
[45] 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
[46] Jin, Q., Dhingra, B., Liu, Z., Cohen, W. W., & Lu, X. (2021). PubMedQA: A dataset for biomedical research question answering. EMNLP 2021, 2567–2577.
[47] Mangalampalli, B. M. (2024). AI-Enhanced Data Governance: Automating Compliance In Healthcare Analytics Platforms. The Review Of DIABETIC STUDIES OPEN ACCESS.
[48] Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.
[49] Nagabhyru, K. C. (2024). Data Engineering in the Age of Large Language Models: Transforming Data Access, Curation, and Enterprise Interpretation. Computer Fraud and Security.
[50] Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731.
[51] Mangala, N. (2024). Leveraging Microsoft Fabric Lakehouse as an AI-Ready Data Platform for Enterprise Analytics. Journal of Information Systems Engineering and Management.
[52] Peddi, R. K. (2024). AI-Based Workforce Analytics for SLA Governance and Uptime Assurance in Data Centers. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8589-8601.
[53] Sasi Kumar Kolla, Venkata Akhilesh Ranga Reddy. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260. https://doi.org/10.70135/seejph.vi.7132
[54] Jensen, P. B., Jensen, L. J., & Brunak, S. (2012). Mining electronic health records: Towards better research applications and clinical care. Nature Reviews Genetics, 13(6), 395–405.
[55] Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2021). What clinicians want: Contextualizing explainable machine learning for clinical end use. Proceedings of Machine Learning Research, 126, 359–380.
[56] Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11), e745–e750.
[57] 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.
[58] Hitzler, P. (2021). Knowledge graphs and their role in explainable artificial intelligence. Frontiers in Big Data, 4, 708949.
[59] Pan, S., Hu, X., Long, G., Jiang, J., Yao, L., & Zhang, C. (2023). Large language models and knowledge graphs: Opportunities and challenges. arXiv preprint arXiv:2308.06374.
[60] Bodenreider, O. (2004). The Unified Medical Language System (UMLS): Integrating biomedical terminology. Nucleic Acids Research, 32(Database Issue), D267–D270.
[61] Rosemann, M. (2024). Process management in the era of autonomous enterprise systems. Business Process Management Journal, 30(4), 945–960.
[62] 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.
[63] Jarrahi, M. H., Lutz, C., Newlands, G., & Lehdonvirta, V. (2024). AI agents and the future of work. Nature Human Behaviour, 8(3), 412–420.
[64] Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
[65] Hevner, A., Storey, V. C., & Ramesh, V. (2024). Designing enterprise systems for autonomous decision intelligence. MIS Quarterly, 48(2), 573–598.
[66] Janssen, M., van der Voort, H., & Wahyudi, A. (2024). Factors influencing adoption of AI-driven autonomous governance systems. Government Information Quarterly, 41(2), 101942.
[67] Wang, Y., Li, Z., Xu, J., & Huang, K. (2024). Agentic workflow automation with large language models. Expert Systems with Applications, 247, 123456.
[68] Siva Hemanth Kolla, Raghunath Loganathan. (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. https://doi.org/10.63278/jicrcr.vi.3786
[69] Hossenfelder, S. (2024). Autonomous AI agents and organizational transformation. AI & Society, 39(2), 457–470.
[70] Bandi, V. D. V. K. (2024). AI-Driven Predictive Risk Modeling Architectures for Financial Systems. International Journal Of Finance, 37(3), 54-78.
[71] Donnelly, K. (2006). SNOMED-CT: The advanced terminology and coding system for eHealth. Studies in Health Technology and Informatics, 121, 279–290.
[72] Inala, R. (2023). Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 579-606.
[73] Schneider, J., Kuss, P., Abraham, R., & Meske, C. (2024). Governance of generative artificial intelligence for companies. arXiv Preprint arXiv:2403.10245.
[74] Harmon, P. (2024). Business process change in the age of generative AI and autonomous systems. Business Process Trends Journal, 22(1), 5–18.
[75] Reddy, V. A. R. (2024). Comparing Batch vs. Streaming Approaches in Healthcare Data Warehousing Environments. Journal of Neonatal Surgery, 13.