深度学习技术用于检测和预测流行病:一个系统的文献综述
Sunday Adeola Ajagbe1,2, Matthew O Adigun2
1Department of Computer & Industrial Production Engineering, First Technical University Ibadan, Ibadan, 200255 Nigeria.
Multimedia tools and applications
|June 26, 2023
概括
深度学习 (DL) 为传染病 (ID) 检测和流行病预测提供了强大的工具. 这个系统的文献审查分析了45篇论文,突出了DLL.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 流行病学 流行病学
背景情况:
- 深度学习 (DL) 在医学中越来越重要,用于及时检测传染病 (ID) 和疾病管理.
- 现有的DL应用程序的流行病预测显示出希望,但有局限性和研究差距.
- 准确,高效和简化的DL系统是流行病检测和预测所需的.
研究的目的:
- 进行关于DL技术在流行病检测和预测中的应用的系统文献审查 (SLR).
- 分析趋势,评估绩效,并确定在流行病监测中DL的局限性.
- 为开发改进的基于DL的系统提供洞察力,以应对未来的流行病准备.
主要方法:
- 对790篇论文的系统文献综述,对45项选定的研究进行了最新的分析.
- 专注于来自Scopus和Web of Science存储库的论文,以获得相关的科学发现.
- 使用表和图表分析DL技术的应用领域,趋势和性能指标.
主要成果:
- DL技术是流行病检测和预测的有效工具.
- 对44项研究的回顾表明,DL技术在这个领域的性能各不相同.
- 关键的挑战包括计算复杂性,不当的数据标签和缺乏高质量的数据集.
结论:
- DL显示了提高流行病检测和预测能力的巨大潜力.
- 未来的研究应该集中在开发改进的DL算法和解决数据质量问题上.
- 考虑建筑修改,比如减少输出层,可能会改善流行性疾病的模型性能.
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