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在生物医学预测系统中使用深度学习算法的趋势

Yanbu Wang1, Linqing Liu2, Chao Wang3

  • 1School of Strength and Conditioning, Beijing Sport University, Beijing, China.

Frontiers in neuroscience
|November 29, 2023
PubMed
概括
此摘要是机器生成的。

本综述探讨了医疗预测系统的深度学习 (DL) 方法. 它强调了DLL.

关键词:
这就是为什么物联网是物联网物联网.生物信息学是一种生物信息学.深度学习是一种深度学习.机器学习是机器学习.医疗信息学是一门医学信息学专业.

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 深度学习 (DL) 在包括医疗预测在内的各个领域都显示出巨大潜力.
  • 将DL集成到医疗系统中,可以实时分析复杂的数据,以改善结果.
  • 目前的研究重点是推进DL应用程序,以提高医疗和医疗保健预测.

研究的目的:

  • 系统地审查医疗和医疗保健预测挑战的最先进的深度学习解决方案.
  • 分类和分析著名的DL方法,如CNN,RNN,GAN,LSTM,SVM和混合模型.
  • 确定应用DL用于医学预测和图像细分的进步,局限性和挑战.

主要方法:

  • 在医疗保健预测中对最近DL应用的综合文献综述.
  • DL方法的分类包括卷积神经网络 (CNN),循环神经网络 (RNN),生成对立网络 (GAN),长短期记忆 (LSTM) 模型,支持矢量机 (SVM) 和混合模型.
  • 基于原则,优点,局限性,方法,模拟环境和数据集的DL模型分析.

主要成果:

  • 大多数审查的研究是在2022年发表的,这表明研究环境正在迅速发展.
  • 关键的DL技术在提高医疗预测系统和运营效率方面表现有前途.
  • 在广泛实施DL方面仍然存在挑战,特别是在医疗图像细分方面.

结论:

  • 深度学习为医疗和医疗保健预测系统提供了重大进步.
  • 需要进一步的研究来克服实施挑战,并充分利用DL在医疗领域的潜力.
  • 评估指标如准确性,精度,特异性和可扩展性对于评估DL模型性能至关重要.