A Layered Data Architecture for Predictive and Personalized Medicine
Keywords:
Predictive Health Analytics,ClinicalData Architecture,Precision Medicine,Clinical Decision Support,Healthcare Data Integration,Predictive Modeling,Electronic Health Records,Artificial Intelligence in Healthcare,Patient Risk Stratification,Personalized Care Support.Abstract
A healthy community is fundamental to a nation's vitality, economic growth, and security, and birth and death records are among the most important indicators of national well-being. Predictive Health seeks to identify the health determinants that shape a community's quality of life and life cycle, drawing on indicators such as population, economic, employment, birth, and death forecasts. It rests on predictive modeling, a methodology that uses historical data to anticipate future outcomes, with forecasting as one of its core components.
The Predictive Health data architecture is designed to support the generation of Health Determinant Indices, thematic maps, forecasts, predictions, and analyses. It supplies vital information for health service planning, policy making, budget preparation, and socio-economic development. By drawing on multiple predictors and a broad range of health determinants, it helps clarify how those determinants affect disease occurrence and how disease vectors can be controlled for prevention. Its emphasis is on forecasting, prediction, and management of health-related matters in order to sustain the health of the whole community.
Predictive modeling of Health Determinant Indices thus contributes significantly to human welfare, enabling governmental and non-governmental health agencies to plan and implement effective measures that promote healthy lives for all.
References
1. Ota, M., & Fujio, K. (2021). Multi-omics approach to precision medicine for immune-mediated diseases. Inflammation and Regeneration, 41, 23.
2. Miao, Z., Humphreys, B. D., McMahon, A. P., & Kim, J. (2021). Multi-omics integration in the age of million single-cell data. Nature Reviews Nephrology, 17, 710–724.
3. Krishna AzithTejaGanti, V., Senthilkumar, K. P., Robinson L, T., Karunakaran, S., Pandugula, C., & Khatana, K. (2024, November). Energy-Efficient Real-Time Hybrid Deep Learning Framework for Adaptive Iot Intrusion Detection with Scalable and Dynamic Threat Mitigation. In Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024).
4. Huang, S., Chaudhary, K., & Garmire, L. X. (2021). More is better: Recent progress in multi-omics data integration methods. Frontiers in Genetics, 12, 638596.
5. Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2021). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 15, 11779322211000359.
6. Li, W., He, X., & He, X. (2021). Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine. Briefings in Bioinformatics, 22(5), bbab024.
7. Santiago-Rodriguez, T. M., & Hollister, E. B. (2021). Multi-omic data integration: A review of concepts, considerations, and approaches. Seminars in Perinatology, 45(8), 151456.
8. Kiran Kumar Maguluri, & Kanthety Sundeep Saradhi. (2024). Leveraging Generative AI and Advanced Predictive Models to Redefine Personalized Medicine and Patient-Centered Care in Modern Healthcare Systems. Utilitas Mathematica, 121, 478–488. Retrieved from https://utilitasmathematica.com/index.php/Index/article/view/2043
9. Mao, R., & Chen, M. (2022). Precision medicine in IBD: Genes, drugs, bugs and omics. Nature Reviews Gastroenterology & Hepatology, 19, 81–82.
10. Pammi, M., Aghaeepour, N., & Neu, J. (2023). Multiomics, artificial intelligence, and precision medicine in perinatology. Pediatric Research, 93, 308–315.
11. Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022). Multimodal machine learning in precision health: A scoping review. npj Digital Medicine, 5, 171.
12. Venkatesh, K. P., Raza, M. M., & Kvedar, J. C. (2022). Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation. npj Digital Medicine, 5, 150.
13. Douglas, M. P., & Kumar, A. (2022). Analyzing precision medicine utilization with real-world data: A scoping review. Journal of Personalized Medicine, 12(4), 557.
14. Avinash Pamisetty, Vijaya Rama Raju Gottimukkala. (2024). Agentic AI-Driven Multi-Cloud Big Data Architecture For Predictive Demand, Credit Risk, And Inventory Financing In National Food Service Supply Chains. Metallurgical and Materials Engineering, 30(4), 959–975. https://doi.org/10.63278/mme.v30i4.1933
15. Liu, F., & Panagiotakos, D. (2022). Real-world data: A brief review of the methods, applications, challenges and opportunities. BMC Medical Research Methodology, 22, 287.
16. Gupta, A., & Kumar, S. (2022). Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review. Computers in Biology and Medicine, 149, 106020.
17. MacFarlane, J., Vullikanti, A., Marathe, M., Eastham, P., Brownstein, J. S., Aguera y Arcas, B., Howell, M. D., & Hernandez, J. (2021). Privacy-first health research with federated learning. npj Digital Medicine, 4, 132.
18. Zayas-Cabán, T., Haque, S. N., & Kemper, N. (2021). Meeting the challenge: Health information technology's essential role in achieving precision medicine. Journal of the American Medical Informatics Association, 28(6), 1345–1352.
19. Shyamala Anto Mary, P., Kalisetty, S., & Mandala, V. M. (2024, November). Advancing IoT Data Forecasting with Deep Learning Framework for Resilience Scalability and Real-World Applications. In Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024).
20. Ferté, T., Viallon, V., Gillaizeau, F., et al. (2021). Automatic phenotyping of electronic health record: PheVis algorithm. Journal of Biomedical Informatics, 117, 103746.
21. Molina, C., & Prados-Suarez, B. (2021). Digital phenotypes for personalized medicine. Studies in Health Technology and Informatics, 285, 141–146.
22. Nadarajah, R., Wu, J., Frangi, A. F., Hogg, D., Cowan, C., & Gale, C. (2021). Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: Protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence. BMJ Open, 11(11), e052887.
23. Lam, C., Siefkas, A., Zelin, N. S., Barnes, G., Dellinger, R. P., Vincent, J.-L., Braden, G., Burdick, H., Hoffman, J., Calvert, J., Mao, Q., & Das, R. (2021). Machine learning as a precision-medicine approach to prescribing COVID-19 pharmacotherapy with remdesivir or corticosteroids. Clinical Therapeutics, 43(5), 871–885.
24. 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.
25. Karhade, A. V., Schwab, J. H., Del Fiol, G., & Kawamoto, K. (2021). SMART on FHIR in spine: Integrating clinical prediction models into electronic health records for precision medicine at the point of care. The Spine Journal, 21(10), 1649–1651.
26. Ahmed, Z. (2024). Multi-omics/genomics in predictive and personalized medicine. In Artificial Intelligence for Drug Product Lifecycle Applications (pp. 109–120). Elsevier.
27. Sharma, A., Lysenko, A., Jia, S., Boroevich, K. A., & Tsunoda, T. (2024). Advances in AI and machine learning for predictive medicine. Journal of Human Genetics, 69, 487–497.
28. Cong, Y., & Endo, T. (2024). A quadruple revolution: Deciphering biological complexity with artificial intelligence, multiomics, precision medicine, and planetary health. OMICS: A Journal of Integrative Biology, 28(6).
29. Acharya, D., & Mukhopadhyay, A. (2024). A comprehensive review of machine learning techniques for multi-omics data integration: Challenges and applications in precision oncology. Briefings in Functional Genomics, 23(5), 549–560.
30. Molla, G., & Bitew, M. (2024). Revolutionizing personalized medicine: Synergy with multi-omics data generation, main hurdles, and future perspectives. Biomedicines, 12(12), 2750.
31. Theodorakis, N., Feretzakis, G., Tzelves, L., Paxinou, E., Hitas, C., Vamvakou, G., Verykios, V. S., & Nikolaou, M. (2024). Integrating machine learning with multi-omics technologies in geroscience: Towards personalized medicine. Journal of Personalized Medicine, 14(9), 931.
32. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.
33. Aldea, M., Friboulet, L., Apcher, S., Jaulin, F., Mosele, F., Sourisseau, T., Soria, J.-C., Nikolaev, S., & André, F. (2023). Precision medicine in the era of multi-omics: Can the data tsunami guide rational treatment decision? ESMO Open, 8(5), 101642.
34. He, X., Liu, X., Zuo, F., Shi, H., & Jing, J. (2023). Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Seminars in Cancer Biology, 88, 187–200.
35. Gupta, N. S., & Kumar, P. (2023). Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine. Computers in Biology and Medicine, 162, 107051.
36. Patton, M. J., & Liu, V. X. (2023). Predictive modeling using artificial intelligence and machine learning algorithms on electronic health record data: Advantages and challenges. Critical Care Clinics, 39(4), 647–673.
37. Ali, A., Al-Rimy, B. A. S., Tin, T. T., Altamimi, S. N., Qasem, S. N., & Saeed, F. (2023). Empowering precision medicine: Unlocking revolutionary insights through blockchain-enabled federated learning and electronic medical records. Sensors, 23(17), 7476.
38. Gupta, N. S., & Kumar, P. (2023). Leveraging data science and novel technologies to develop and implement precision medicine strategies in critical care. Critical Care Clinics, 39(4), 675–692.
39. Moor, M., Banerjee, O., Shakeri Hossein Abad, Z., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616, 259–265.
40. Wang, D., & Zhang, S. (2024). Large language models in medical and healthcare fields: Applications, advances, and challenges. Artificial Intelligence Review, 57, 299.
41. Meng, X., Yan, X., Zhang, K., Liu, D., Cui, X., Yang, Y., Zhang, M., Cao, C., Wang, J., Wang, X., Gao, J., Wang, Y.-G.-S., Ji, J.-M., Qiu, Z., Li, M., Qian, C., Guo, T., Ma, S., Wang, Z., Guo, Z., Lei, Y., Shao, C., Wang, W., Fan, H., & Tang, Y.-D. (2024). The application of large language models in medicine: A scoping review. iScience, 27(5), 109713.
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