Isaksen Jonas L, Nørregaard Malene, Manninger Martin, Dobrev Dobromir, Jespersen Thomas, Hermans Ben, Heijman Jordi, Plank Gernot, Scherr Daniel, Pock Thomas, Thambawita Vajira, Riegler Michael A, Kanters Jørgen K, Linz Dominik
Laboratory of Experimental Cardiology, University of Copenhagen, Copenhagen, Denmark.
Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
Int J Cardiol Heart Vasc. 2025 Aug 30;60:101783. doi: 10.1016/j.ijcha.2025.101783. eCollection 2025 Oct.
Machine learning methods are increasingly used in cardiovascular research. In order to highlight opportunities and challenges of the evaluation of studies applying machine learning, we use examples from cardiac electrophysiology, a field characterized by large and often imbalanced amounts of data. We provide recommendations and guidance on evaluating and presenting supervised machine learning studies. We recommend proper cohort selection, keeping training and testing data strictly separate, and comparing results to a reference model without machine learning as basic principles to ensure the quality of studies using machine learning methods. We furthermore recommend specific metrics and plots when reporting on machine learning including on models for multi-channel time series or images. This Best Practice paper represents a possible blueprint to help evaluate machine learning-based medical tests in cardiac electrophysiology and beyond.
机器学习方法在心血管研究中的应用越来越广泛。为了突出评估应用机器学习的研究的机遇和挑战，我们以心脏电生理学为例，该领域的特点是数据量大且往往不均衡。我们为评估和展示监督式机器学习研究提供建议和指导。我们建议选择合适的队列，严格将训练数据和测试数据分开，并将结果与无机器学习的参考模型进行比较，作为确保使用机器学习方法的研究质量的基本原则。此外，我们建议在报告机器学习时使用特定的指标和图表，包括多通道时间序列或图像的模型。本最佳实践文件代表了一个可能的蓝图，有助于评估心脏电生理学及其他领域基于机器学习的医学测试。
