用机器学习工具解决抗菌素耐药性"大流行":现有证据的总结
Doris Rusic1, Marko Kumric2,3, Ana Seselja Perisin1
1Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia.
Microorganisms
|May 25, 2024
概括
机器学习 (ML) 提供了强大的工具来打击抗菌素耐药性 (AMR),这是一个主要的全球健康威胁. 本综述探讨了ML在预测耐药性,发现新药和加快研究以克服耐药性感染方面的应用.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
背景情况:
- 抗菌素耐药性 (AMR) 是一个关键的全球健康威胁.
- 有大量的数据 (EHR,基因组数据) 可用于抗菌药物管理.
- 目前对抗抗菌素耐药性的现有策略不足.
研究的目的:
- 突出机器学习 (ML) 在抗药性研究中的机会.
- 为现场提供当前ML应用的概述.
- 鼓励研究人员采用新的ML方法.
主要方法:
- 关于AMR中ML的现有文献的叙述性审查.
- 分析ML在预测耐药性,预测易感性和药物发现方面的应用.
- 识别ML应用程序开发和实施之间的差距.
主要成果:
- ML可以从基因组数据中预测AMR,并预测药物易感性.
- ML有助于识别疫情模式进行监测.
- ML加速了新的抗菌治疗方法的发现.
- 在AMR研究中实际实施ML工具方面存在很大的差距.
结论:
- 机器学习为打击抗微生物药物耐药性提供了重大机会.
- 机器学习工具有可能彻底改变对抗超级细菌的斗争.
- 需要进一步的研究和实施,以充分利用AMR中的ML.
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