从数据到决策:利用人工智能和机器学习来打击抗菌素耐药性 - - 一个全面的审查
José M Pérez de la Lastra1, Samuel J T Wardell2, Tarun Pal3
1Biotechnology of Macromolecules, Instituto de Productos Naturales y Agrobiología, IPNA (CSIC), Avda. Astrofísico Francisco Sánchez, 3, 38206, San Cristóbal de la Laguna, (Santa Cruz de Tenerife), Spain. jm.perezdelalastra@csic.es.
Journal of medical systems
|August 1, 2024
概括
人工智能 (AI) 和机器学习 (ML) 正在彻底改变抗菌素耐药性 (AMR) 管理. 这些技术有助于病原体识别,耐药性模式分析和新型抗生素发现,以获得更好的治疗结果.
科学领域:
- 微生物学 微生物学
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
背景情况:
- 耐药细菌构成了全球主要的健康威胁.
- 抗菌素耐药性 (AMR) 需要创新的解决方案.
- 人工智能 (AI) 和机器学习 (ML) 提供先进的计算方法.
研究的目的:
- 审查AI/ML在抗菌素耐药性 (AMR) 管理中的应用.
- 突出AI/ML在病原体识别,耐药性模式分析,治疗预测和新抗生素发现方面的作用.
- 评估AI/ML对AMR管理的当前和未来影响.
主要方法:
- 审查最近在AI/ML算法应用到AMR的进展.
- 分析AI/ML在处理大规模基因组和临床数据集的能力.
- 探索AI/ML在药物发现和治疗优化方面的潜力.
主要成果:
- 通过AI/ML,可以对大型数据集进行高效的分析,从而预测AMR趋势和治疗反应.
- 机器学习算法识别了针对抗生素耐药性的基因标记.
- 在优化药物管理和发现新型抗生素方面,AI/ML显示出前景.
结论:
- AI/ML显著增强了对抗菌素耐药性的管理.
- 这些技术有助于诊断,严重性评估和选择抗微生物疗法.
- 随着进一步的进展,预计AI/ML在AMR管理中的广泛临床应用.
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