观点:通过人工智能/机器学习来限制抗微生物耐药性
Daniel Amsterdam1,2,3
1Jacobs School of Medicine & Biomedical Sciences, State University of New York, Buffalo, NY, USA.
BME frontiers
|January 8, 2024
概括
本综述探讨了60年来临床微生物学中的计算机应用,重点是人工智能 (AI) 和机器学习 (ML) 对于抗生素选择,以对抗日益增长的抗菌素耐药性.
科学领域:
- 临床微生物学 临床微生物学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 抗微生物药物耐药性日益严重的全球威胁需要先进的战略来有效地使用抗生素.
- 早期的专家系统,如Mycin (1976年) 显示出希望,但在临床整合方面遇到了局限性.
- 机器学习 (ML) 通过结合复杂变量来克服专家系统的限制.
研究的目的:
- 审查60年来临床微生物学中计算机应用的演变.
- 突出AI和ML在指导细菌感染抗生素选择方面的潜力.
- 讨论人工智能在发现新型抗菌剂中的作用.
主要方法:
- 在临床微生物学中计算机应用的历史审查.
- 讨论AI/ML计划设计,包括各种临床和流行病学变量.
- 探索AI在抗菌药物发现中的作用.
主要成果:
- 像Mycin这样的早期AI系统受限于基于规则的约束.
- 综合性AI/ML程序可以整合影响细菌/药物相互作用的多个因素.
- 人工智能对识别新的抗微生物疗法充满希望.
结论:
- 人工智能和机器学习对于优化抗生素选择和打击抗菌素耐药性至关重要.
- 未来AI/ML在临床微生物学中的应用可以提高治疗结果和药物发现.
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