开发机器学习算法,以扩大抗生素管理的规模
Tam Tran-The1, Eunjeong Heo2, Sanghee Lim1
1Enolink Inc., Cambridge, USA.
International journal of medical informatics
|November 23, 2023
概括
可解释的机器学习模型将患者优先考虑抗生素管理干预措施,提高效率并识别更多与传统方法相比需要降级或停止治疗的病例.
科学领域:
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
- 机器学习用于抗生素管理.
背景情况:
- 抗生素管理计划 (ASP) 对于减少不适当的抗生素使用至关重要.
- ASP的劳动密集性限制了它们的广泛采用.
- 可解释的机器学习 (ML) 模型提供了一个解决方案,以优先考虑患者的干预.
研究的目的:
- 引入可解释的ML模型,以优先考虑住院患者进行抗生素管理干预.
- 通过确定最能受益的患者来提高ASP活动的效率.
主要方法:
- 在大量住院患者 (超过13万名患者日) 上训练了极端梯度增强 (XGB) 和轻梯度增强机器 (LGBM) 模型.
- 利用了160多个功能,包括处方,实验室,微生物学和患者状况数据.
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
主要成果:
- 模型显示出强大的预测性能 (IV至PO的AUROC:0.81,早期降级:0.78,晚期降级:0.72,停止:0.80).
- 与传统策略相比,确定了更多的停止 (41%) 和IV到PO切换 (16%) 的病例.
- SHAP分析为模型预测提供了临床相关的解释,与ASP团队的专业知识保持一致.
结论:
- 可解释的ML模型可以显著提高ASP的效率.
- 这些模型优先考虑患者进行有针对性的干预,如停止或缓解升级.
- 整合ML为抗生素管理提供了一个可扩展的方法.
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