对ECG分类的不确定性意识域调整
概括
不确定性目标增强了不同数据集中的心律失常的自动心电图 (ECG) 分类. 这些方法通过调整来自各种来源的数据来提高诊断准确性,使人工智能在现实世界的临床环境中更可靠.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 自动心电图 (ECG) 分类有助于心血管诊断,但在不同的临床环境 (领域) 中与数据变化作斗争.
- 机器学习 (ML) 模型的现实世界部署受到领域转移的阻碍,其中数据与培训条件不同.
- 使用易于获得的未标记目标数据进行无监督域调整 (UDA) 可以克服这些局限性.
研究的目的:
- 调查不确定性目标在改善无监督域适应的有效性,以在新的心电图数据集上进行自动化心律失常分类.
- 开发和验证新的UDA方法,在迪里克莱特优先网络框架内利用不确定性量化.
- 通过解决领域转移挑战,增强ML模型用于心血管诊断的临床适用性.
主要方法:
- 将迪里克莱特前置网络应用于来自MIT-BIH和圣彼得堡INCART心律失常数据集的ECG数据.
- 实施了两种基于不确定性的UDA方法:最小化对准目标域不确定性和对准源/目标类预测不确定性.
- 评估模型性能使用F1分数进行三元心律失常分类,并与基线和最先进的方法进行比较.
主要成果:
- 第一种方法,最大限度地减少目标域的不确定性,改善了三元性心律失常分类的基线目标F1得分7%.
- 第二种方法,调整类预测不确定性,提高了3%的最新领域适应性能.
- 这两种方法都在不同医院的心电图上证明了心跳分类准确度的提高,这表明域名适应成功.
结论:
- 不确定性目标为提高自动心律失常分类模型的稳定性和通用性提供了一个有希望的策略.
- 拟议的UDA方法有效地解决了ECG数据的域变化,促进了人工智能驱动的诊断工具的更广泛的临床采用.
- 利用未标记的目标域数据与不确定性量化对于心脏病学中的真实世界临床机器学习应用至关重要.
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