抗菌耐药性和机器学习:过去,现在和未来
Faiza Farhat1, Md Tanwir Athar2, Sultan Ahmad3,4
1Department of Zoology, Aligarh Muslim University, Aligarh, India.
Frontiers in microbiology
|June 12, 2023
概括
本文献统计综述强调了机器学习在预测抗微生物药物耐药性的使用日益增加. 美国在这个新兴科学领域的研究成果中处于领先地位.
科学领域:
- * 计算生物学和生物信息学.
- * 传染病研究.
- *数据科学在医疗保健中的应用.
背景情况:
- * 机器学习越来越多地用于预测抗菌素耐药性.
- * 这是关于这个快速发展的研究领域的第一个文献统计综述.
- *了解研究趋势和关键参与者对于未来的进步至关重要.
研究的目的:
- * 进行第一个机器学习在抗菌素耐药性预测中的文献分析.
- * 确定该领域的领先国家,组织,期刊和作者.
- * 分析研究趋势,合作和引用网络.
主要方法:
- * 文献计量指标:文章数量,引用数量,希尔什指数 (H-指数).
- *使用的软件:VOSviewer和Biblioshiny用于网络和趋势分析.
- *数据来源:分析与机器学习和抗菌素耐药性相关的出版物.
主要成果:
- * 美国以254篇文章 (37.57%) 领先,其次是中国和英国.
- * 爱思唯尔,斯普林格自然,MDPI和边界媒体公司是顶级出版商.
- *"微生物学边界"和"科学报告"是主要的期刊.
- * 研究和出版物显著增加.
- * 专注于先进的算法,以准确预测阻力.
结论:
- *用于预测抗微生物药物耐药性的机器学习领域正在迅速扩大.
- * 美国是研究成果的主要贡献者.
- * 持续开发复杂的机器学习算法对于打击抗生素耐药性至关重要.
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