预测抗生素耐药性从使用机器学习的监测数据的抗生素敏感性测试结果
Swetha Valavarasu1, Yasaswini Sangu1, Tanmaya Mahapatra2
1Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani, 333031, Rajasthan, India.
Scientific reports
|August 20, 2025
概括
人工智能 (AI) 可以预测细菌的抗生素耐药性. 机器学习模型,特别是XGBoost,使用瑞ATLAS数据集显示出高准确性,将抗生素确定为关键预测因素.
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) 是一个重大的全球卫生挑战.
- 人工智能 (AI) 为开发用于打击AMR的先进工具提供了新的机会.
研究的目的:
- 应用机器学习技术来预测细菌的抗生素耐药性.
- 为了分析瑞ATLAS抗生素数据集,其中包括超过90万个细菌分离物.
主要方法:
- 在两个数据集上利用了机器学习模型,包括XGBoost:只有表型和表型 + 基因型.
- 进行探索性数据分析,预处理,模型训练,验证和超参数优化.
- 使用的SHAP总结图为模型的可解释性.
主要成果:
- XGBoost表现出优异的性能,AUC值为0.96 (仅表型) 和0.95 (表型+基因型).
- 超参数调整略有提高了准确性;数据平衡增强了回忆.
- 使用的特定抗生素被确定为预测耐药性的最关键特征.
结论:
- 人工智能驱动的方法显示了预测细菌抗生素耐药性的巨大潜力.
- 结果提供了对全球AMR模式的洞察力,并可以为临床决策和政策制定提供信息.
- 高度细粒度的数据集对于有效的AI驱动的AMR缓解策略至关重要.
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