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抗微生物最小抑制度可以从表型数据中使用随机森林方法计算出.

Gayatri Anil1,2, Joshua Glass1, Abdolreza Mosaddegh1,3

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机器学习模型准确地预测了抗微生物耐药性 (AMR) 数据,认为缺失的最小抑制度. 这有助于改善人类,动物和环境部门的抗菌药物耐药性趋势分析和监测.

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抗微生物耐药性 (AMR) 是一种归算是指指责一个人.机器学习是机器学习.最低抑制度的最小抑制度随机的森林随机的森林

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科学领域:

  • 兽医微生物学 兽医微生物学
  • 公共卫生 公共卫生
  • 计算生物学是一种计算生物学.

背景情况:

  • 抗菌素耐药性 (AMR) 构成重大公共卫生威胁,需要跨部门监测.
  • 数据挑战,如缺失的值和协议变化,阻碍了准确的AMR趋势分析.
  • 机器学习提供了一个潜在的解决方案,用于赋予缺少的抗微生物敏感性数据.

研究的目的:

  • 开发和评估机器学习模型,用于归因缺失的最小抑制度 (MIC).
  • 用内部和外部数据集评估这些模型的准确性.
  • 加强对抗药物耐药性趋势的评估,并为公共卫生政策提供信息.

主要方法:

  • 随机森林模型使用来自国家抗菌素耐药性监测系统的牛相关的大肠杆菌数据进行训练.
  • 模型根据隔离元数据和其他MIC预测了10种抗微生物药物的MIC.
  • 在持有测试数据和来自和人类的外部数据集上验证了性能.

主要成果:

  • 在测试数据上,模型在所有10种抗微生物药物上都达到了80%以上的准确性,在5种抗微生物药物上达到90%以上的准确性.
  • 六个模型在测试,人类和数据集中展示了一致的性能.
  • 四个模型在人类数据上显示了类似的准确性,但在数据上显示了更低的准确性.

结论:

  • 开发的机器学习模型准确地预测MIC值,适合在AMR监测中归纳缺少的数据.
  • 准确的归算可以改善抗菌耐药性趋势的评估,告知管理人员,并简化易感性测试.
  • 这些模型为增强抗菌耐药性监测和公共卫生战略提供了有前途的工具.