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预测低敏度远程医疗患者的早期恶化,使用渐变增强.

Ricardo Ricci Lopes, Holly Chavez, Louis Atallah

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括

    一个新的渐变增强早期预警分数 (EWS) 模型有效地检测患者在远程医疗监测中的恶化,在较低敏度单位中优于修改早期预警分数 (MEWS*).

    科学领域:

    • 医疗信息学 医疗信息学
    • 临床决策支持 临床决策支持
    • 医疗保健中的人工智能

    背景情况:

    • 早期识别生理异常对于及时干预和预防患者不良结果至关重要.
    • 远程医疗监测使用人口管理来远程识别不稳定的患者.
    • 较低的敏度单位需要特定的工具,以便及时检测患者的病情恶化.

    研究的目的:

    • 提出和评估一种使用梯度增强的新型早期预警分数 (EWS) 模型.
    • 加强对患者病情恶化的检测,特别是在远程医疗监测下的医疗/外科病房.
    • 将拟议模型的性能与修改的早期预警分数 (MEWS*) 进行比较.

    主要方法:

    • 利用来自eICU研究院数据库的36963例患者接触的数据集.
    • 开发了一个渐变增强模型,包含来自人口统计,生命体征和实验室数据的35个特征.
    • 将拟议的模型与MEWS* (考虑到年龄和氧和) 进行比较,以预测患者的病情恶化.

    主要成果:

    • 拟议的模型在24小时的预损坏后实现了AUROC 0.79和AUPRC 0.28,超过了MEWS* (AUROC 0.67,AUPRC 0.07).
    • 在一个小时的预损坏时间内,该模型达到AUROC为0.86和AUPRC为0.42,与MEWS*相比 (AUROC 0.74,AUPRC 0.21).

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  • 梯度增强模型在预测患者病情恶化方面表现出卓越的性能.

    • 结论:

    • 拟议的梯度增强EWS模型显示了改善远程医疗环境中患者病情恶化的早期检测的显著前景.
    • 这种模型对敏度较低的患者特别有效,比MEWS*等现有得分提供了增强的预测能力.
    • 未来的研究应该解决缺少的数据,持续监测和临床工作流集成问题.