AI-driven risk stratification for targeted public health interventions
1 Department of Operations and Information Systems, Information Systems, University of Utah, Utah, USA.
2 Department of Medicine and Surgery, Edo State University, Uzairue, Edo, Nigeria.
Review
World Journal of Biology Pharmacy and Health Sciences, 2024, 20(02), 988-994.
Article DOI: 10.30574/wjbphs.2024.20.2.0901
Publication history:
Received on 04 October 2024; revised on 13 November 2024; accepted on 16 November 2024
Abstract:
The increasing complexity of public health challenges, particularly during infectious disease outbreaks, necessitates innovative approaches to identify and protect vulnerable populations. This study explores the use of supervised machine learning algorithms to stratify populations based on risk factors and predict severe outcomes during outbreaks. By leveraging demographic, clinical, and socioeconomic data, the proposed AI-driven models aim to enable healthcare systems to prioritize vulnerable groups, allocate resources effectively, and implement preventive measures. The results demonstrate the potential of AI in reducing mortality, improving health equity, and enhancing the overall resilience of public health systems. This research contributes to the growing body of knowledge on data-driven decision-making in public health.
Keywords:
Artificial Intelligence; Risk Stratification; Supervised Machine Learning; Public Health Interventions; Health Equity; Resource Allocation; Outbreak Management
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0