AUTOMATION OF EPIDEMIOLOGICAL ANALYSIS USING MEDICAL DATABASES AND ARTIFICIAL INTELLIGENCE
Abstract
Epidemiological analysis is fundamental for understanding disease patterns, identifying risk factors, and guiding public health interventions. Traditional epidemiological research often relies on manual data collection and statistical analysis, which can be time-consuming, error-prone, and limited in scope. The integration of Artificial Intelligence (AI) with large-scale medical databases enables automated, high-precision epidemiological analysis, enhancing disease surveillance, outbreak prediction, and resource allocation. This thesis examines the role of AI-driven automation in epidemiological studies, the methodologies employed, applications in infectious and chronic disease monitoring, and the challenges and future directions of this emerging field. Automated AI approaches offer opportunities to accelerate public health responses, improve data-driven decision-making, and advance predictive epidemiology.
References
Char, D.S., Shah, N.H. and Magnus, D. (2018) ‘Implementing Machine Learning in Health Care — Addressing Ethical Challenges,’ New England Journal of Medicine, 378(11), pp. 981–983.
Chien, L.C. and Yu, H.L. (2019) ‘Data Mining for Real-Time Surveillance of Infectious Disease Outbreaks,’ International Journal of Environmental Research and Public Health, 16(4), 554.
Esteva, A. et al. (2019) ‘A Guide to Deep Learning in Healthcare,’ Nature Medicine, 25, pp. 24–29.
Gordis, L. (2014) Epidemiology. 5th edn. Philadelphia: Elsevier.
Miotto, R., Li, L., Kidd, B.A. and Dudley, J.T. (2016) ‘Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records,’ Scientific Reports, 6, 26094.
Rajkomar, A., Dean, J. and Kohane, I. (2019) ‘Machine Learning in Medicine,’ New England Journal of Medicine, 380, pp. 1347–1358.
Topol, E.J. (2019) Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
Wang, Y., Kung, L., Byrd, T.A. (2020) ‘Big Data Analytics: Understanding Its Capabilities and Potential Benefits for Healthcare Organizations,’ Technological Forecasting and Social Change, 126, pp. 3–13.
Yang, W., Zhang, D., Peng, L. et al. (2020) ‘Epidemic Prediction Using Artificial Intelligence: COVID-19 Case Study,’ Frontiers in Public Health, 8, 222.
Zou, H., Hastie, T., Tibshirani, R. (2020) ‘Statistics for High-Dimensional Data: Methods and Applications,’ Annual Review of Statistics and Its Application, 7, pp. 1–32.
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Copyright (c) 2025 Fazliddin Arziqulov, Sayfullayeva Dilbar Izzatillayevna, Maxsudov Valijon Gafurjonovich (Author)
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