从原始临床MALDI-TOF质谱数据预测抗生素耐药性的多标签分类
César A Astudillo1, Xaviera A López-Cortés2,3, Elias Ocque1
1Computer Science Department, Engineering Faculty, Universidad de Talca, Talca, Chile.
Scientific reports
|December 29, 2024
概括
多标签分类有效预测关键细菌的抗生素耐药性 (AMR),与传统方法相匹配. 这种方法更好地反映了复杂的AMR数据,以改善诊断和临床决策.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 抗菌素耐药性 (AMR) 是一个关键的全球健康威胁,需要先进的预测工具.
- 准确预测抗生素耐药性对于有效的临床决策至关重要.
- 当前的预测模型经常使用单标签方法,这可能无法完全捕捉AMR的复杂性.
研究的目的:
- 研究多标签分类作为预测抗生素耐药性的新方法.
- 为了比较四种关键细菌的多标签分类与单标签方法的性能.
- 通过使用外部数据集来评估模型的概括性和稳定性.
主要方法:
- 从DRIAMS存储库中使用多个数据集进行培训和验证.
- 评估了四种机器学习算法:多层感知器,支持向量分类器,随机森林和极端梯度提升.
- 实施单标签和多标签分类框架.
- 研究过量采样技术,并开发了一种可重复的 MALDI-TOF 数据处理方法.
主要成果:
- 与单一标签模型相比,多标签分类显示出具有竞争力的性能.
- 在大多数情况下,单标签和多标签方法之间没有观察到统计学上显著的性能差异.
- 在对外部数据集 (DRIAMS B和C) 进行验证时,模型显示出良好的概括性和稳定性.
- 多标签框架有效地捕捉了AMR数据的相互联系性质.
结论:
- 多标签分类提供了一个有希望的途径,以提高AMR研究的预测准确性.
- 这种方法更准确地反映了现实世界AMR数据的复杂性.
- 这些发现支持开发改进的诊断工具和临床干预策略,以对抗AMR.
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