联合机器学习用于预测重症患者急性损伤:台湾的一项多中心研究
Chun-Te Huang1,2, Tsai-Jung Wang2, Li-Kuo Kuo3
1Institute of Emergency and Critical Care Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan.
Health information science and systems
|October 12, 2023
概括
联合学习 (FL) 实现了全医院的新型急性损伤 (AKI) 预测模型. 这种方法提高了重症患者的AKI预测准确度,提高了临床决策.
科学领域:
- 临界护理医学 临界护理医学
- 医疗保健中的人工智能
- 腎臟健康 腎臟健康
背景情况:
- 急性损伤 (AKI) 在重症监护病房 (ICU) 中构成了重大挑战.
- 预测建模的传统数据共享受到隐私和后勤方面的担忧的阻碍.
- 开发强大的AKI预测模型需要多样化的多中心数据集.
研究的目的:
- 使用联合学习 (FL) 在重症患者中开发急性损伤 (AKI) 预测的综合模型.
- 克服跨医院的数据共享挑战,以改善AKI预测.
- 建立一个可靠的AKI预测模型,利用多机构数据.
主要方法:
- 雇佣联合学习 (FL) 来训练台湾多家医院的AKI预测模型.
- 利用了台中退伍军人总医院 (TCVGH) 和其他四个转诊中心的电子医疗记录.
- 使用机器学习算法开发和验证了完整的 (60 个特征) 和节的 (21 个特征) 模型.
主要成果:
- 在使用极端梯度增强,神经网络和随机森林的完整和节模型中实现了可比的预测准确度 (AUROC ~ 0.90).
- 对节模型的外部验证显示,四家医院的AUROC值从0.760到0.865不等.
- 基于神经网络的FL在参与中心的模型性能略有改善.
结论:
- 为ICU患者成功开发了一种可靠的AKI预测模型,具有24小时的预测时间.
- 新的FL平台提高了AKI预测模型在多个机构的性能.
- 联合学习为医疗保健中的协作模型开发提供了一个可行的解决方案,而无需直接共享数据.
相关概念视频
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