一个机器学习模型来预测医院医药患者的最佳抗生素使用情况
Nathan Radakovich1,2, Priya Prasad1, Daniel Escobar2
1Department of Medicine, University of California, San Francisco, CA, USA.
Antimicrobial stewardship & healthcare epidemiology : ASHE
|October 13, 2025
概括
一个新的机器学习模型有效地识别了不必要的抗生素订单,改进了抗生素管理计划 (ASP). 这种人工智能工具有助于针对性审查,以获得更好的患者结果和医疗保健系统效率.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 抗生素管理局 抗生素管理局
背景情况:
- 低于最佳的抗生素使用导致患者和卫生系统的不良结果.
- 抗生素管理计划 (ASP) 旨在通过前性审计和反来减少不必要的抗生素使用.
- 当前的ASP方法在没有预先选过程的情况下是无效的.
研究的目的:
- 开发一种机器学习模型,根据其需要抗生素管理计划审查的可能性对抗生素订单进行分层.
- 确定影响模型对抗生素必要性和最佳使用的预测的关键因素.
主要方法:
- 一个机器学习模型使用来自单一学术医院的专家标记的点患病率数据进行训练.
- 来自电子健康记录的数据,包括生命体征,实验室和微生物学数据,自动汇总.
- 传染病专家将必要性和最佳使用的抗生素订单标记为基本事实.
主要成果:
- 该模型实现了高性能:AUC为0.89的必要性和0.80的最佳使用.
- 关键预测因素包括临床不稳定性,炎症和感染的标志物.
- 简单的临床感染指数显示没有预测能力.
结论:
- 使用常规可用的EHR数据的预测模型可以识别最有可能从ASP审查中受益的抗生素订单.
- 这种方法使得早期识别和干预成为可能,提高ASP的效率.
- 该模型显示了优化抗生素使用和改善患者结果的前景.
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