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EAPR:可解释和增强的患者代表性学习,用于疾病预测.

Jiancheng Zhang1,2, Yonghui Xu1,2, Bicui Ye3,4

  • 1Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Jinan, China.

Health information science and systems
|November 17, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了可解释和增强的患者代表性学习 (EAPR),以改善使用电子健康记录 (EHR) 的疾病预测. EAPR增强了患者数据的表现,并提供了可解释的模型,即使数据有限或缺失.

关键词:
数据增强数据增强疾病预测 疾病预测解释方法方法的解释.患者代表是患者的代表.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 医疗保健中的机器学习
  • 计算医学是一种计算医学.

背景情况:

  • 患者代表性学习编码电子健康记录 (EHR) 用于疾病预测.
  • 深度学习增强了EHR的代表性,但需要大量的数据集,缺乏可解释性.
  • 缺失或不足的患者数据阻碍了强大的代表性学习.

研究的目的:

  • 提出一种可解释和增强的患者代表学习 (EAPR) 方法来预测疾病.
  • 在患者代表性学习中解决有限数据和模型不可解释性的挑战.
  • 提高疾病预测模型的性能和可解释性.

主要方法:

  • EAPR采用可信度区间控制的数据增强,以增强有限数据的患者表示.
  • 使用两阶段梯度反向传播技术来确保模型可解释性.
  • 该方法是使用现实世界的临床数据来验证的.

主要成果:

  • EAPR有效地提高了患者代表性学习,特别是在数据不足的情况下.
  • 拟议的方法显著改善了疾病预测模型的性能.
  • 实验结果证明了EAPR方法的可解释性.

结论:

  • 在疾病预测中,EAPR为患者代表性学习提供了一个强大的解决方案.
  • 该方法成功地平衡了数据增强与模型可解释性.
  • EAPR促进了医疗保健中可靠和可解释的AI的发展.