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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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相关实验视频

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基于Grad-CAM的可解释的人工智能与医学文本处理相关.

Hongjian Zhang1, Katsuhiko Ogasawara1

  • 1Graduate School of Health Science, Hokkaido University, N12-W5, Kitaku, Sapporo 060-0812, Japan.

Bioengineering (Basel, Switzerland)
|September 28, 2023
PubMed
概括

可解释的人工智能 (XAI) 通过使用梯度加权类激活映射 (Grad-CAM) 来增强医学深度学习,以实现直观的决策. ResNet模型在医学文本分类中实现了高精度,提高了模型的透明度.

科学领域:

  • 人工智能的人工智能
  • 医疗信息学 医疗信息学
  • 计算机视觉 计算机视觉

背景情况:

  • 深度学习的不透明性阻碍了医疗应用,需要可解释AI (XAI).
  • XAI确保人类对医疗保健中人工智能模型决策的理解.
  • 想象模型的注意力对于信任和验证至关重要.

研究的目的:

  • 开发和评估用于医学文本分类的XAI系统.
  • 提高医疗保健中的深度学习模型的透明度.
  • 为了直观地呈现AI驱动的医学文本分析的基础.

主要方法:

  • 从计算机视觉 (ResNet) 转移学习到医疗文本任务.
  • 使用梯度加权类激活映射 (Grad-CAM) 进行可视化.
  • 将Word2Vec,BERT,ResNet,1D CNN和Bi-LSTM进行比较,以进行文本分类.

主要成果:

  • 在正式医疗文本上进行预训练的ResNet获得了最高的性能 (90.9%的回忆,91.1%的精度,90.2%的F1得分).
  • 在模型预测中,Grad-CAM可视化有效地突出了重要的单词.
  • 开发的系统证明了高精度的分类与可解释的结果.
关键词:
这是Grad-CAM.这就是ResNet ResNet.可解释的人工智能 (XAI)文字处理器是文字处理器.

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结论:

  • ResNet与Grad-CAM相结合,为医学文本分类提供了一个强大的XAI解决方案.
  • 这种方法提高了AI在医学中的解释性和可靠性.
  • 这种方法为医疗应用的AI决策过程提供了直观的见解.