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混合价值意识的变压器架构,用于从纵向和非纵向临床数据的联合学习
Yijun Shao1,2, Yan Cheng1,2, Stuart J Nelson1
1Department of Clinical Research and Leadership, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA.
研究人员开发了一种新的混合价值意识变压器 (HVAT) 模型来分析复杂的临床数据. 这种深度学习方法显示出预测阿尔茨海默氏症等疾病的希望,使用电子健康记录.
科学领域:
- 人工智能在医学中的应用
- 为医疗保健提供深度学习.
- 临床数据分析 临床数据分析
背景情况:
- 变压器模型已经彻底改变了自然语言处理.
- 将变压器架构适应复杂的纵向临床数据提出了独特的挑战.
- 现有的方法可能无法完全捕捉临床数据的细微差别,包括数值.
研究的目的:
- 为临床数据设计一种基于变压器的新型深度神经网络 (DNN) 架构.
- 开发一种能够从纵向和非纵向临床数据共同学习的模型.
- 解决临床数据的复杂性,例如与医学概念相关的数值.
主要方法:
- 引入了混合价值意识变压器 (HVAT),一种新的DNN架构.
- HVAT 独特地从临床代码的数值 (例如,实验室结果) 中学习.
- 使用一种灵活的纵向数据表示,称为"临床令牌".
主要成果:
- 一个HVAT模型的原型被训练在一个病例控制数据集上.
- 在预测阿尔茨海默病和相关痴呆症方面取得了很高的表现.
- 证明了该模型在各种临床数据学习任务中的潜力.
结论:
- 混合价值意识变压器 (HVAT) 对于分析复杂的临床数据是有效的.
- 由于HVAT能够整合数值和灵活的数据表示,因此提高了预测能力.
- 这种深度学习方法显示了促进临床信息学和疾病预测的巨大潜力.
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