导航未来:机器学习在彻底改变抗菌药物管理和感染预防和控制方面的作用
John J Hanna1,2, Richard J Medford1,2
1Division of Infectious Diseases, Department of Internal Medicine, Brody School of Medicine.
Current opinion in infectious diseases
|May 31, 2024
概括
机器学习 (ML) 通过改善监测和优化治疗来增强感染预防和控制 (IPC) 和抗菌药物管理 (ASP). 克服数据和整合挑战有望带来更安全的医疗保健和更好的抗抗菌素耐药性患者结果.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 感染预防和控制 (IPC) 和抗微生物管理 (ASP) 对于对抗医疗相关感染和抗微生物耐药性至关重要.
- 传统方法在精度和效率方面存在局限性,需要先进的分析方法.
研究的目的:
- 审查机器学习 (ML) 在IPC和ASP中的当前应用和未来潜力.
- 突出ML的能力,以提高针对感染和耐药性的干预措施的准确性,速度和影响.
主要方法:
- 对IPC和ASP中ML应用的当前文献的审查.
- 分析ML在预测分析,自然语言处理和个性化医学中的作用.
主要成果:
- 在感染监测,风险预测和抗菌药物使用优化方面,ML显示出前景.
- 关键的挑战包括数据质量,模型解释性,伦理问题和临床工作流集成.
结论:
- 在IPC和ASP中ML的未来是有希望的,取决于跨学科的合作.
- ML 准备推进个性化医疗,实时监测和更安全的医疗环境,这对于解决全球抗菌素耐药性至关重要.
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