一个基于机器学习的临床决策支持系统,以改善终身护理
Robert P Pierce1, Adam Kell2, Bernie Eskridge3
1Department of Family and Community Medicine, University of Missouri, Columbia, Missouri, United States.
Applied clinical informatics
|November 7, 2025
概括
实施机器学习模型来预测死亡风险并没有改善终身护理 (EoLC) 文档. 该系统未能增强预先指示,息护理或DNAR订单,这表明临床决策支持集成存在挑战.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 临床决策支持系统 临床决策支持系统
- 机器学习在医学中的应用
背景情况:
- 终身护理 (EoLC) 改善了患者的结果,降低了成本,但未得到充分利用.
- 机器学习 (ML) 模型可以识别高风险患者,以便及时进行EoLC干预.
研究的目的:
- 评估基于ML的死亡预测模型对学术卫生系统内的EoLC提供的影响.
主要方法:
- 一个随机森林ML模型被整合到临床决策支持系统中.
- 间断时间序列分析比较了实施前和实施后的EoLC文档化率.
- 结果包括预先指示,息护理咨询和高风险患者的DNAR订单.
主要成果:
- 预先指令文档在实施后得到了改进,但其他EoLC措施没有.
- 机器学习模型的预测性能在生产过程中明显差于在开发过程中.
- 由于缺乏临床益处,该系统在16个月后被撤回.
结论:
- 基于ML的临床决策支持系统可能无法有效地改善EoLC的提供.
- 系统层面的因素,CDS设计和模型性能差,都会阻碍成功实施.
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