用机器学习推动心血管医学的发展：进展、潜力和展望。
Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective.
机构信息
Department of Medicine, Division of Cardiology, University of California, San Francisco, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158, USA.
Department of Medicine, Division of Cardiology, University of California, San Francisco, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158, USA.
出版信息
Cell Rep Med. 2022 Dec 20;3(12):100869. doi: 10.1016/j.xcrm.2022.100869.
Recent advances in machine learning (ML) have made it possible to analyze high-dimensional and complex data-such as free text, images, waveforms, videos, and sound-in an automated manner by successfully learning complex associations within these data. Cardiovascular medicine is particularly well poised to take advantage of these ML advances, due to the widespread digitization of medical data and the large number of diagnostic tests used to evaluate cardiovascular disease. Various ML approaches have successfully been applied to cardiovascular tests and diseases to automate interpretation, accurately perform measurements, and, in some cases, predict novel diagnoses from less invasive tests, effectively expanding the utility of more widely accessible diagnostic tests. Here, we present examples of some impactful advances in cardiovascular medicine using ML across a variety of modalities, with a focus on deep learning applications.
近年来，机器学习（ML）的发展使得我们可以通过成功学习这些数据中的复杂关联，以自动化的方式分析高维且复杂的数据，例如自由文本、图像、波形、视频和声音。心血管医学特别适合利用这些 ML 进展，因为医疗数据的广泛数字化以及用于评估心血管疾病的大量诊断测试。各种 ML 方法已成功应用于心血管测试和疾病，以实现自动解释、准确测量，并且在某些情况下，可以从侵入性较小的测试中预测新的诊断，从而有效地扩展了更广泛可用的诊断测试的应用。在这里，我们展示了使用 ML 在各种模式下在心血管医学方面的一些有影响力的进展的示例，重点是深度学习应用。
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