Adaptive Intelligence Synthesis System for Anticipatory Public Health Optimization
DOI:
https://doi.org/10.5281/zenodo.22111640Keywords:
Predictive modelling, knowledge fusion, health informatics, population health, risk stratification, Health Information Exchange, Health Maintenance Organization, Longitudinal Health Survey of Veterans .Abstract
Tasked with reconciling a growing multitude of health data and knowledge sources, the current study presents a framework for the autonomous fusion of advanced health knowledge to habilitate predictive optimization of population health. An ontology-based predictive modeling methodology is applied in modeling and quantifying multi-factorial health outcomes and the potential impact of targeted interventions. Major types of potential data, knowledge, and technology sources are identified, and issues directly related to data and knowledge integration addressed. The authors’ evolving knowledge-integration system Structural Health, for example, is designed to continuously update advanced causal knowledge by fusing human- and machine-generated evidence. Risk stratification, enabling optimal targeting of health promotion resources, is another directly consequential application. Predictive optimization of population health has clear and important ethical, legal, and social implications, particularly potential bias and inequitable impact of predictive stratification.
Increased availability of detailed electronic health records opens up new opportunities for implementing risk-stratification models that identify the patients at highest future risk of sudden deterioration in health status who may benefit most from targeted interventions. However, the associated economic considerations indicate that unless resources devoted to health promotion can be best targeted where they will make the greatest difference, implementation is unlikely to be economically justifiable. Hence, PHF can be predictive population health aiming to recommend therefore the allocation of health-promotion resources or efforts to achieve the greatest overall impact on population health. Predictive optimization of population health is directly consequential. As a concept it has appeared in diverse domains requires finally integrating predictive-health-stratification models in the domain of data-driven artificial intelligence. Nevertheless, a detailed theoretical and methodological formulation, has yet to be realized.
References
1. Arık, S. Ö., Shor, J., Sinha, R., Yoon, J., Ledsam, J. R., Le, L. T., Dusenberry, M. W., Yoder, N. C., Popendorf, K., Epshteyn, A., Euphrosine, J., Kanal, E., Jones, I., Li, C.-L., Luan, B., Mckenna, J., Menon, V., Singh, S., Sun, M., ... Pfister, T. (2021). A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. npj Digital Medicine, 4, 146.
2. Gunasekeran, D. V., Tseng, R. M. W. W., Tham, Y.-C., Wong, T. Y., & others. (2021). Applications of digital health for public health responses to COVID-19: A systematic scoping review of artificial intelligence, telehealth and related technologies. npj Digital Medicine, 4, 40.
3. Ganesh, S. K., Subbareddy, K., Gopi, A., Davuluri, P. N., Mannar, B. R., & Karnawat, A. T. (2026, June). Fraudulent Credit Card Transaction Detection Using a Hybrid Ensemble-Anomaly Detection Framework. In 2026 6th International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE.
4. Friedman, J., Liu, P., Troeger, C. E., Carter, A., Reiner, R. C., Jr., Barber, R. M., Collins, J., Lim, S. S., Pigott, D. M., Vos, T., Hay, S. I., Murray, C. J. L., & Gakidou, E. (2021). Predictive performance of international COVID-19 mortality forecasting models. Nature Communications, 12, 2609.
5. Hassan, E. M., & Mahmoud, H. N. (2021). Orchestrating performance of healthcare networks subjected to the compound events of natural disasters and pandemic. Nature Communications, 12, 1338.
6. Dawadi, D., Yandamuri, U. S., V, S. K., Stalin, J. L. A., & Naveenkumar, R. (2026). Privacy-Aware Edge-Based Intelligent Video Analytics for Scalable Crowd Management in Smart Cities. In 2026 3rd International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1–7). IEEE. 2026 3rd International Conference on Integrated Intelligence and Communication Systems (ICIICS). https://doi.org/10.1109/iciics67880.2026.11483444
7. Papoutsoglou, G., Karaglani, M., Lagani, V., Thomson, N., Røe, O. D., Tsamardinos, I., & Chatzaki, E. (2021). Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets. Scientific Reports, 11, 15107.
8. Reynolds, Z., Vullikanti, A. K., Wang, L., & Marathe, M. (2021). Forecasting influenza activity using machine-learned mobility map. Nature Communications, 12, 726.
9. Alali, Y., Harrou, F., & Sun, Y. (2022). A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Scientific Reports, 12, 2467.
10. Nandan, B. P., Kumar, M. V. K., Garapati, R. S., Bandi, V. D. V. K., Davuluri, P. S. L. N., & Mangalampalli, B. M. (2026). AI-Enhanced Semiconductor Yield Optimization Using Hybrid Deep Learning and Edge Data Analytics. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1–6). IEEE. 2026 IEEE International Conference on AI Engineering and Innovations (AIEI). https://doi.org/10.1109/aiei69164.2026.11497190
11. Fisher, S., & Rosella, L. C. (2022). Priorities for successful use of artificial intelligence by public health organizations: A literature review. BMC Public Health, 22, 2146.
12. Ward, T., Johnsen, A., Ng, S., & Chollet, F. (2022). Forecasting SARS-CoV-2 transmission and clinical risk at small spatial scales by the application of machine learning architectures to syndromic surveillance data. Nature Machine Intelligence, 4, 814–827.
13. Bedi, B., Yandamuri, U. S., Kummari, D. N., Nagubandi, A. R., Amistapuram, K., & D, A. (2026). AI-Driven Sentiment and Behavior Analysis for Sustainable Business Growth. In 2026 International Conference on Emerging Research in Smart Electronics and Machine Informatics (ECMI) (pp. 1–11). IEEE. 2026 International Conference on Emerging Research in Smart Electronics and Machine Informatics (ECMI). https://doi.org/10.1109/ecmi68341.2026.11603189
14. Yi, S. E., Harish, V., Gutierrez, J., & others. (2022). Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: Derivation and validation cohort study. BMJ Open, 12, e051403.
15. McClymont, H., Lambert, S. B., Barr, I., Vardoulakis, S., Bambrick, H., & Hu, W. (2024). Internet-based surveillance systems and infectious diseases prediction: An updated review of the last 10 years and lessons from the COVID-19 pandemic. Journal of Epidemiology and Global Health, 14, 645–657.
16. Roberts, M. C., Holt, K. E., Del Fiol, G., Baccarelli, A. A., & Allen, C. G. (2024). Precision public health in the era of genomics and big data. Nature Medicine, 30, 1865–1873.
17. Kolla, S. K., Khaparkar, S., Kumar, D., Yadav, S., Shankar, S., & Shamila, M. (2026, June). Deep Learning Based Real-Time Threat Monitoring for Cloud and IoT-Enabled Healthcare Systems. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-6). IEEE.
18. Rodríguez, A., Kamarthi, H., Agarwal, P., Ho, J., Patel, M., Sapre, S., & Prakash, B. A. (2024). Machine learning for data-centric epidemic forecasting. Nature Machine Intelligence, 6, 1122–1131.
19. Chumachenko, D., Morita, P. P., Ghaffarian, S., & Chumachenko, T. (2024). Editorial: Artificial intelligence solutions for global health and disaster response: Challenges and opportunities. Frontiers in Public Health, 12, 1439914.
20. Gore, M. N., & Olawade, D. B. (2024). Harnessing AI for public health: India's roadmap. Frontiers in Public Health, 12, 1417568.
21. Reis, M., Reis, F., & Kunde, W. (2024). Influence of believed AI involvement on the perception of digital medical advice. Nature Medicine, 30, 3098–3100.
22. Suprith, M., Davuluri, P. N., Paramasamy, S., Yandamuri, U. S., & Motamary, S. (2026, June). AI-Enabled Business Process Reengineering for Agile and Data-Driven Organizations. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-6). IEEE.
23. Silva Sepulveda, R., & Boman, M. (2025). Multimodal machine learning for analysing multifactorial causes of disease—The case of childhood overweight and obesity in Mexico. Frontiers in Public Health, 12, 1369041.
24. Meyer, A. G., Lu, F., Clemente, L., & Santillana, M. (2025). A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data. Epidemics, 50, 100816.
25. Mendes, V. I. S., Mendes, B. M. F., Moura, R. P., Lourenço, I. M., Oliveira, M. F. A., Ng, K. L., & Pinto, C. S. (2025). Harnessing artificial intelligence for enhanced public health surveillance: A narrative review. Frontiers in Public Health, 13, 1601151.
26. Sanku, R., Kolla, S. H., Karri, S., & AS, Y. (2026, February). An Intelligent Analytics-Aware Cloud-Cluster Framework for Large-Scale Data Analytics. In 2026 3rd International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-7). IEEE.
27. Villanueva-Miranda, I., Xiao, G., & Xie, Y. (2025). Artificial intelligence in early warning systems for infectious disease surveillance: A systematic review. Frontiers in Public Health, 13, 1609615.
28. Li, Y.-H., Agarwal, N., & Agarwal, S. (2025). Advancement in public health through machine learning: A narrative review of opportunities and ethical considerations. Journal of Big Data, 12.
29. Du, M. (2026). Time series-based forecasting of infectious disease outbreak using information systems in public health. Frontiers in Public Health, 13, 1680534.
30. Singh, K. U., Gupta, S. K., Dubey, Y., & Mangalampalli, B. M. (2026, June). Enhancing Healthcare Cyber Defense with Machine Learning-Based Attack Prediction and Prevention. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-6). IEEE.
31. Murph, A. C., Beesley, L. J., Gibson, G. C., Castro, L. A., Del Valle, S. Y., & Osthus, D. (2026). A disease-agnostic approach to ensemble learning for infectious disease forecasting. Nature Communications, 17, 4255.
32. Papakonstantinou, M., Zoulias, E., Gallos, P., & Mantas, J. (2026). Development of a decision support system for predicting the evolution of epidemics using open-source software tools. Studies in Health Technology and Informatics.
33. Raj, T., Philip, A., & Thomas, V. (2026). Artificial intelligence in public health—Challenges and opportunities. European Journal of Clinical Nutrition, 80, 566–573.
34. Gao, Q., Chen, L., & Huang, Z. (2026). Opportunities and challenges of artificial intelligence in public health: A systematic review on technological efficacy, ethical dilemmas, and governance pathways. Frontiers in Public Health, 13.
35. Campbell, E. A., Goodtree, H., Gillani, S., Oyefolu, O., Kelly, A., Rivers, C., & Watson, C. (2026). Impact, use, and implications of artificial intelligence in public health decision making by elected officials: A scoping review. Frontiers in Public Health, 14.
36. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
37. Olawade, D. B., Wada, O. J., David-Olawade, A. C., Kunonga, E., Abaire, O., & Ling, J. (2023). Using artificial intelligence to improve public health: A narrative review. Frontiers in Public Health, 11, 1196397.
38. Bashingwa, J. J. H., Mohan, D., Chamberlain, S., & others. (2023). Can we design the next generation of digital health communication programs by leveraging the power of artificial intelligence to segment target audiences, bolster impact and deliver differentiated services? A machine learning analysis of survey data from rural India. BMJ Open, 13, e063354.
39. Hassan, M. D. N., & others. (2026). Digital epidemiology: Utilizing big data for public health surveillance and disease outbreak prediction. Journal of the Association for Medical Informatics in Asia.
Additional Files
Published
Data Availability Statement
None
Issue
Section
License
Copyright (c) 2026 Ganesh Pambala (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons
Attribution 4.0 International License (CC BY 4.0).
You are free to:
- Share: copy and redistribute the material
- Adapt: remix, transform, and build upon the material
for any purpose, even commercially.
Under the following terms:
Attribution — You must give appropriate credit to
the original author(s) and source.
Full license text: https://creativecommons.org/licenses/by/4.0/
