MELEP:在多标签心电图诊断中可转移的新型预测措施
Cuong V Nguyen1, Hieu Minh Duong1, Cuong D Do1,2
1College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam.
Journal of healthcare informatics research
|August 12, 2024
概括
我们开发了MELEP,这是一个新的指标,用于评估多标签心电图 (ECG) 诊断的可转移性. MELEP有效地预测了新ECG数据上的模型性能,帮助预先训练的模型选择.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 电心电图 (ECG) 解释面临挑战,因为注释数据有限.
- 转移学习对于ECG分析至关重要,但评估可转移性仍然不发达.
- 现有的方法缺乏用于多标签心电图分类的具体指标.
研究的目的:
- 引入MELEP (经验预测的Muti-label预期日志),这是评估知识转移有效性的新型指标.
- 为在多标签心电图诊断中预先训练的模型提供计算效率和通用度的测量.
- 解决ECG解释中的深度学习模型的可转移性评估的差距.
主要方法:
- 开发了MELEP,这是一种需要预训练模型一次向前传球的指标.
- 测试了MELEP在各种下游多标签ECG诊断任务上的有效性.
- 使用微调深度神经网络 (CNN和RNN) 评估了MELEP与实际模型性能的相关性.
主要成果:
- MELEP在小,不平衡的心电图数据集上对预训练模型的性能表现出了强大的预测能力.
- 在MELEP分数和平均F1分数之间观察到高相关系数 (绝对值>0.6).
- 在不同的深度学习架构 (卷积和循环网络) 中,MELEP 证明了其有效性.
结论:
- MELEP是第一个专门用于多标签心电图分类的可转移度量.
- MELEP可以显著加快选择合适的预训练模型进行心电图诊断.
- 这一指标减少了对广泛微调的需求,节省了计算资源和时间.
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