用变压器模型进行深度合体学习,以增强阿尔茨海默病检测
Shiza Latif1, Naeem Ul Islam2, Zaki Uddin1
1NUST College of Electrical and Mechanical Engineering, Rawalpindi, Pakistan.
Scientific reports
|July 9, 2025
概括
通过使用基于BERT的新型深度学习模型,提升了早期阿尔茨海默病 (AD) 检测. 这种整体方法提高了从临床记录中诊断AD的准确性,有助于及时干预.
科学领域:
- 计算神经科学是一种计算神经科学.
- 医学中的人工智能
- 医疗保健的自然语言处理.
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,没有治愈方法.
- 早期检测和干预对于管理AD进展至关重要.
- 目前的诊断方法需要改进,以提高准确性和效率.
研究的目的:
- 用文本临床数据开发和评估一种用于早期阿尔茨海默病诊断的新型深度学习模型.
- 为了提高诊断性能,利用数据增强和集体学习方法.
- 为了增强功能提取和文本理解,以便更准确地检测AD.
主要方法:
- 对文本数据应用了数据增强技术.
- 提出了一个基于BERT的双分支深度学习模型,包括BERT编码器,卷积,LSTM和反复卷积神经网络层.
- 一种集体学习方法将两个分支的输出融合在一起,形成了一个强大的分类.
- 该模型是根据阿兹海默症患者和健康对照者的临床笔记进行训练的.
主要成果:
- 拟议的整体模型在DementiaBank Pitt Corpus的Cookie Theft子集上实现了高性能.
- 获得了94.98%的准确性,0.9523的F1得分和0.93的AUC.
- 与现有的最先进的AD早期诊断模型相比,表现出卓越的性能.
结论:
- 开发的基于BERT的组合模型显示了对阿尔茨海默病的准确和高效的早期诊断的重大前景.
- 使用先进的深度学习技术进行文本数据分析可以有效地帮助AD检测.
- 这种方法为AD护理中的临床决策和患者管理提供了有价值的工具.
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