机器学习在电子健康记录数据上的应用,以打击抗生素耐药性
Samuel E Blechman1, Erik S Wright1,2
1Department of Biomedical Informatics, University of Pittsburgh, Pennsylvania.
The Journal of infectious diseases
|July 12, 2024
概括
人工智能 (AI) 和机器学习 (ML) 显示出使用电子健康记录改善抗微生物药物耐药性策略的希望. 然而,实际挑战限制了这些强大的预测模型的广泛临床采用.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 临床研究 临床研究
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 对临床应用越来越感兴趣.
- 计算机和机器学习框架的进步促进了使用电子健康记录 (EHR) 数据进行预测模型培训.
研究的目的:
- 提供适用于EHR数据的ML技术的入门教程.
- 探索ML在解决抗菌素耐药性 (AMR) 的应用.
主要方法:
- 针对EHR数据挑战的ML方法的审查.
- 在AMR中使用EMR数据用于ML模型的案例研究:预测病原体携带/感染,优化经验治疗,并支持抗菌管理.
主要成果:
- 可以使用EHR数据构建ML模型,用于各种与AMR相关的任务.
- 证明了ML在促进适当使用抗菌素方面的潜力.
结论:
- 药物管理对加强抗微生物药物管理和打击耐药性具有重大前景.
- 机器学习模型的临床部署因实际和实施障碍而面临限制.
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