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机器学习多模式模型用于妄想风险分层的分层.

Joseph I Friedman1,2, Prathamesh Parchure3, Fu-Yuan Cheng3

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此摘要是机器生成的。

这项研究开发了一种机器学习 (ML) 模型,用于在医院自动化妄想风险分层. 该模型在实践中表现良好,改善了痴呆症检测率,并可能减少药物使用.

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科学领域:

  • 临床信息学 临床信息学
  • 医疗保健中的人工智能
  • 患者安全 患者安全

背景情况:

  • 医院 Delirium 是一个常见的并发症,影响患者的结果.
  • 使用机器学习 (ML) 来自动识别 Delirium 风险可以改善早期干预.
  • 关于ML模型在真实临床环境中的痴呆风险分层表现的有限数据存在.

研究的目的:

  • 开发,运行和验证一种多模式的ML模型,用于非重症监护病房的妄想风险分层.
  • 评估模型对临床工作流程和患者结果的影响.
  • 评估模型在实时临床实践中的表现.

主要方法:

  • 使用自动化电子医疗记录和自然语言处理进行质量改进研究.
  • 开发了一种ML模型,该模型基于2016-2020年期间入院的60岁以上患者的数据进行训练.
  • 在实时临床实践中验证了该模型 (2023年3月至2024年3月),并将结果与ML前队列进行了比较.

主要成果:

  • 在ML模型下,曲线下的面积达到0.94 (95%CI,0.93-0.95).
  • 每月妄检测率从4.42% (ML前) 显著增加到17.17% (ML后) (P<.001).
  • 后ML队列与ML前队列相比,接受了更低的每日剂量二类药物和奥兰zapine.

结论:

  • 一个新的多式机器学习模型可以在现实临床实践中有效地自动化妄风险分层.
  • 该模型证明了可行性和可接受的性能,有助于 Delirium 识别和护理.
  • 这种方法可以优化资源配置,以加强妄想管理.