评估心脏病学中人工智能支持的医学测试:最佳实践
Jonas L Isaksen1, Malene Nørregaard1, Martin Manninger2
1Laboratory of Experimental Cardiology, University of Copenhagen, Copenhagen, Denmark.
International journal of cardiology. Heart & vasculature
|September 11, 2025
概括
本研究提供了在心血管研究中评估机器学习的最佳实践,特别是心脏电生理学. 关键建议包括严格的数据分离和与非机器学习模型的比较,以确保强大的研究质量.
科学领域:
- 心血管研究研究心血管研究
- 生物医学数据科学 生物医学数据科学
- 心脏电生理学 心脏电生理学
背景情况:
- 机器学习 (ML) 越来越多地应用于心血管研究.
- 由于大量,不平衡的数据集,心脏电生理学提出了独特的挑战.
- 在这个领域评估ML研究需要具体的指导方针.
研究的目的:
- 突出在心血管研究中评估ML研究的机遇和挑战.
- 为评估心脏电生理学中的ML应用提供关于最佳实践的指导.
- 建立基于ML的医学试验质量评估的框架.
主要方法:
- 利用了来自心脏电生理学研究的例子.
- 专注于监督机器学习研究评估.
- 制定了关于ML研究报告和介绍的建议.
主要成果:
- 推适当的队列选择和训练/测试数据的严格分离.
- 支持与非ML参考模型进行比较.
- 建议用于报告ML模型性能的具体指标和图表,特别是时间序列和图像数据.
结论:
- 遵守推的原则确保了ML研究的质量.
- 建议的最佳实践是评估心脏电生理学和相关领域的ML的蓝图.
- 标准化的评估方法将促进ML在心血管医学中的可靠应用.
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