人工智能在药物耐药性管理中的作用
Amir Elalouf1, Hadas Elalouf1, Ariel Rosenfeld2
1Department of Management, Bar-Ilan University, 5290002 Ramat Gan, Israel.
包括机器学习 (ML) 在内的人工智能 (AI) 正在通过预测耐药性和识别新抗生素来彻底改变抗菌素耐药性 (AMR) 管理. 人工智能为全球公共卫生提供了有希望的干预措施,尽管数据和伦理方面的挑战存在.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 抗菌素耐药性 (AMR) 构成了全球健康的重大威胁,需要创新的管理策略.
- 传统的抗抗菌耐药性方法越来越不足,推动了对先进的计算方法的需求.
研究的目的:
- 审查人工智能 (AI) 的应用,特别是深度学习和机器学习 (ML),以解决抗菌素耐药性 (AMR).
- 突出AI在预测耐药性模式,识别新型抗生素和优化抗生素使用方面的作用.
主要方法:
- 对应用AI/ML算法 (原始贝斯,决策树,随机森林,SVM,ANN) 到AMR数据的研究进行审查.
- 分析人工智能对预测耐药性表型,识别候选药物和检测AMR相关突变的影响.
主要成果:
- 人工智能模型显著改善了对抗微生物药物耐药性模式的预测.
- 机器学习算法已经成功识别了新型抗生素候选者,并优化了抗生素的使用.
- 人工智能有助于检测与AMR相关的突变,提供对抗性机制和传播的洞察力.
结论:
- 人工智能是打击AMR的强大工具,有可能改善患者的治疗结果和疾病管理.
- 克服数据稀缺,隐私,伦理考虑和促进合作等挑战对于实现AI在AMR中的全部潜力至关重要.
- 在AMR管理中的AI应用对全球公共卫生战略有重大影响.
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