阿德伯特:使用预先训练的语言模型来预测从轻度认知障碍到阿尔茨海默病的进展
Chengsheng Mao1, Jie Xu2, Luke Rasmussen1
1Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
Journal of biomedical informatics
|July 10, 2023
概括
一个新的深度学习模型,AD-BERT,有效地使用电子健康记录预测阿尔茨海默病从轻度认知障碍的进展. 这种方法增强了阿尔茨海默病的早期检测和干预策略.
科学领域:
- 计算神经科学是一种计算神经科学.
- 医疗信息学医学信息学
- 医疗保健中的人工智能
背景情况:
- 轻度认知障碍 (MCI) 是阿尔茨海默病 (AD) 的前身.
- 预测MCI到AD的进展对于及时干预至关重要.
- 电子健康记录 (EHR) 包含有价值的非结构化的临床笔记,用于疾病预测.
研究的目的:
- 开发一个深度学习框架,用EHR临床笔记来预测MCI到AD的进展.
- 利用变压器的双向编码器表示 (BERT) 来提高预测准确性.
主要方法:
- 一个基于BERT的模型 (AD-BERT) 在3657名MCI患者的非结构化临床笔记上进行了预训练.
- 临床笔记被预处理,去识别,并嵌入向量表示.
- 该模型计算了MCI到AD进展的概率,在2563名MCI患者的单独队列上得到验证.
主要成果:
- 在西北医学企业数据仓库 (NMEDW) 和威尔康奈尔医学 (WCM) 数据集中,AD-BERT的表现超过了7个基线模型.
- 实现了0.849 (NMEDW) 和0.883 (WCM) 的接收器操作特征曲线 (AUC) 下的面积.
- 获得了0.440 (NMEDW) 和0.680 (WCM) 的F1分数,证明了强大的预测能力.
结论:
- 该研究强调了使用电子健康记录和预训练的语言模型在阿尔茨海默病研究中的潜力.
- 在预测MCI-AD进展方面,AD-BERT表现出卓越的性能.
- 这种方法对改善早期AD检测和干预策略有重大影响.
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