基于优化的深度LSTM组合模型和多变量时间序列数据融合的计算机辅助进展检测模型
Hager Saleh1, Eslam Amer2, Tamer Abuhmed3
1Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt.
Scientific reports
|September 28, 2023
概括
这项研究引入了一个新的深度学习框架,用于早期发现阿尔茨海默病. 新型组合模型使用患者时间序列数据显著提高了预测准确性,有助于临床决策.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 阿尔茨海默病 (AD) 是导致痴呆的主要原因,需要早期和准确的检测,以便及时干预.
- 目前用于AD检测的机器学习和深度学习 (DL) 模型在临床环境中显示出有限的性能和稳定性.
- 集体学习方法往往优于独立模型,这表明改善AD预测的潜力.
研究的目的:
- 提出和评估一个新的深层堆叠框架,用于准确预测阿尔茨海默病的早期预测.
- 利用多变量时间序列患者数据,包括神经成像和认知得分,以提高AD检测.
- 开发一个强大的模型,用于管理AD进展的临床决策支持.
主要方法:
- 利用一个深层堆叠组合框架,结合多个长短期记忆 (LSTM) 深度学习模型.
- 采用贝叶斯优化来调整各个LSTM基础分类器在不同的特征集.
- 在异质患者数据上训练异质基准模型,以捕捉复杂的纵向模式.
- 在国家阿尔茨海默氏症协调中心数据集中的685名患者队列上评估了整体模型.
主要成果:
- 提出的深层堆叠组合模型实现了高性能指标:82.02%的精度,82.25%的精度,82.02%的回忆,82.12%的F1-score.
- 在预测阿尔茨海默病方面表现优于经典机器学习模型和个别LSTM分类器.
- 与文献中的现有最先进的方法相比,表现出卓越的性能.
结论:
- 新的深层堆叠框架在早期发现阿尔茨海默病方面取得了重大进展.
- 整体模型的高精度和稳定性使其成为临床决策支持的有希望的工具.
- 进一步开发可能会导致一个可靠的系统,协助专家监测AD进展.
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