后冠状病毒肺炎智能公共卫生管理

Anika Tabassum Bristy, Rahatara Ferdousi, A. Hossain, M. Islam, M. A. Hossain, Abdulmotaleb El Saddik
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引用次数: 0

摘要

流行病爆发是持续的全球化、城市化、人口流动、气候变化、人口结构变化和新型传染因子演变的集体影响,导致高发病率、高死亡率和巨大的经济损失,例如COVID-19。因此,对疾病出现的早期预测可以在防止疾病成为流行病方面发挥关键作用。基于边缘人工智能的解决方案已被提出用于使用机器学习(ML)进行医疗保健预测。在本文中,我们的重点是提出基于ML的公共卫生高级模型,以减少和控制流行病的爆发。来自相互关联的学科、共享的数据存储库和不同角色的集体知识已经嵌入到提议的框架中。基于COVID-19相关实际数据的评估表明,ML可用于公共卫生数据的COVID风险预测,并可在早期采取预防措施应对疫情。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Post COVID-19 Intelligent Public Healthcare Management
Epidemic outbreaks are collective effects of ongoing globalization, urbanisation, population mobility, climate change, demographic change and evolution of newer strains of infectious agents that result in high morbidity, mortality and huge financial loss, such as COVID-19. Thus, the early prediction of the emergence of a disease can play a pivotal role to prevent a disease to become epidemic. The Edge AI based solution has been proposed for healthcare prediction using machine learning (ML). In this paper, our focus is to propose ML based advanced model for public healthcare to reduce and control epidemic outbreaks. Collective knowledge from interconnected disciplines, shared data repository, and diverse roles have been embedded into the proposed framework. An evaluation based on actual COVID-19 related data demonstrates that ML can be used for COVID risk prediction for public health data as well as to take preventive steps to combat epidemics in early-stage.
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