AI-Driven Strategies for Precision Public Health Interventions
DOI:
https://doi.org/10.71222/bc2bkp70Keywords:
Precision Public Health, artificial intelligence, risk stratification, spatiotemporal surveillance, chronic disease management, Heterogeneous Data ArchitectureAbstract
The transition from traditional "one-size-fits-all" public health measures to Precision Public Health (PPH) is necessitated by the "Data-Rich, Information-Poor" (DRIP) syndrome, where massive healthcare data volumes fail to translate into rapid response times. This study proposes an AI-driven framework utilizing a Heterogeneous Data Architecture (HDA) to integrate multi-modal streams, including electronic health records (EHRs), environmental IoT telemetry, and digital phenotyping. To address infectious disease dynamics, a hybrid spatiotemporal model merging SEIR compartmental logic with Long Short-Term Memory (LSTM) networks was developed, achieving a +7-day lead-time advantage and an 80% reduction in transmission velocity. For chronic disease management, an unsupervised clustering approach identified four distinct patient phenotypes, including a critical "Invisible" high-risk group whose clinical markers are borderline but whose environmental stressors predict rapid deterioration. Empirical results demonstrate that while precision interventions incur higher upfront costs per capita, they reduce the "Effective Cost per Successful Outcome" by 33% to 70% across various domains. This framework not only stabilizes the healthcare system capacity Kcap but also provides a proactive blueprint for modernizing public health infrastructure through algorithmic stratification and personalized intervention pathways.
References
1. J. V. Cordeiro, “Artificial intelligence and precision public health: A balancing act of scientific accuracy, social responsibility, and community engagement,” Portuguese Journal of Public Health, vol. 42, no. 1, pp. 1–5, 2024.
2. M. R. Bosward, A. Braunack-Mayer, M. E. Frost, and S. Carter, “The emergence and future of precision public health: A scoping review,” Health Policy and Technology, Art. no. 101056, 2025.
3. P. E. Velmovitsky, T. Bevilacqua, P. Alencar, D. Cowan, and P. P. Morita, “Convergence of precision medicine and public health into precision public health: Toward a big data perspective,” Frontiers in Public Health, vol. 9, Art. no. 561873, 2021.
4. M. N. Kamel Boulos and P. Zhang, “Digital twins: From personalised medicine to precision public health,” Journal of Personalized Medicine, vol. 11, no. 8, Art. no. 745, 2021.
5. D. D. Farhud and S. Zokaei, “Ethical issues of artificial intelligence in medicine and healthcare,” Iranian Journal of Public Health, vol. 50, no. 11, p. i, 2021.
6. Y. Y. Aung, D. C. Wong, and D. S. Ting, “The promise of artificial intelligence: A review of the opportunities and challenges of artificial intelligence in healthcare,” British Medical Bulletin, vol. 139, no. 1, pp. 4–15, 2021.
7. K. P. Venkatesh, M. M. Raza, and J. C. Kvedar, “Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation,” NPJ Digital Medicine, vol. 5, no. 1, Art. no. 150, 2022.
8. A. Nayarisseri, R. Khandelwal, P. Tanwar, M. Madhavi, D. Sharma, G. Thakur, et al., “Artificial intelligence, big data and machine learning approaches in precision medicine and drug discovery,” Current Drug Targets, vol. 22, no. 6, pp. 631–655, 2021.
9. Y. Kumar, A. Koul, R. Singla, and M. F. Ijaz, “Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework and future research agenda,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 7, pp. 8459–8486, 2023.
10. F. Wong, C. de la Fuente-Nunez, and J. J. Collins, “Leveraging artificial intelligence in the fight against infectious diseases,” Science, vol. 381, no. 6654, pp. 164–170, 2023.
11. L. H. Nazer, R. Zatarah, S. Waldrip, J. X. C. Ke, M. Moukheiber, A. K. Khanna, et al., “Bias in artificial intelligence algorithms and recommendations for mitigation,” PLOS Digital Health, vol. 2, no. 6, Art. no. e0000278, 2023.
12. J. Iqbal, D. C. Cortés Jaimes, P. Makineni, S. Subramani, S. Hemaida, T. R. Thugu, et al., “Reimagining healthcare: Unleashing the power of artificial intelligence in medicine,” Cureus, vol. 15, no. 9, Art. no. e44658, 2023.
13. K. Murphy, E. Di Ruggiero, R. Upshur, D. J. Willison, N. Malhotra, J. C. Cai, et al., “Artificial intelligence for good health: A scoping review of the ethics literature,” BMC Medical Ethics, vol. 22, no. 1, Art. no. 14, 2021.
14. M. Jeyaraman, S. Balaji, N. Jeyaraman, and S. Yadav, “Unraveling the ethical enigma: Artificial intelligence in healthcare,” Cureus, vol. 15, no. 8, 2023.
15. C. Delpierre and T. Lefèvre, “Precision and personalized medicine: What their current definition says and silences about the model of health they promote: Implication for the development of personalized health,” Frontiers in Sociology, vol. 8, Art. no. 1112159, 2023.
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Copyright (c) 2025 Yehao Wang, Zihang Ma, Yue Xing, Bingxin Li (Author)
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