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SimTA++:用于临床异步时间序列的简单注意力神经网络.

Zhihao Li1, Jingyu Li1, Kaiming Kuang2

  • 1National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence and School of Computer Science, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China; Hubei Luojia Laboratory, Wuhan, China; JD Explore Academy, China.

Neural networks : the official journal of the International Neural Network Society
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建模复杂的临床时间序列数据具有挑战性. 简单的时间注意力 (SimTA) 和SimTA++有效地模拟异步的医疗数据,优于预测免疫治疗反应的现有方法.

关键词:
不同步的时间序列.临床时间数据.免疫治疗是一种免疫疗法.专注于自己的注意力在 SimTA SimTA 里面.

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科学领域:

  • * 计算生物学 * 计算生物学
  • * 医学信息学 医学信息学
  • * 机器学习 * 机器学习

背景情况:

  • * 临床时间序列数据由于多模式和不规则的特征而存在独特的挑战.
  • *现有的深度学习模型难以应对异步临床数据的复杂性.
  • *来自其他领域的先进的顺序数据处理方法尚未在临床环境中完全实现.

研究的目的:

  • * 开发用于建模异步临床时间序列数据的新型深度学习方法.
  • * 介绍简单的时间注意力 (SimTA) 和其增强版本,SimTA++.
  • * 评估SimTA++的性能与各种数据集的既定模型相比.

主要方法:

  • *开发SimTA,一个时间注意模块,通过时间依赖的注意力机制建模异步时间步骤.
  • *扩展到SimTA++,将非线性时间关注纳入非单调关系.
  • *使用循环神经网络,图形神经网络,时间融合变压器和神经微分方程进行比较分析.

主要成果:

  • *SimTA++在三个基准数据集中表现出与现有方法相比的优异性能.
  • *该方法在合成数据集PhysioNet 2019和免疫疗法反应数据集上显示出有效性.
  • *SimTA++作为免疫疗法反应的预测性多omics生物标志物取得了有希望的结果.

结论:

  • *SimTA++提供了一个强大的解决方案,用于模拟异步的,多模式的临床时间序列数据.
  • * 拟议的方法促进了深度学习在临床信息学中的应用.
  • *SimTA++显示了提高临床决策中的预测准确度的潜力,特别是在免疫治疗反应方面.