Hwang Minki, Leem Chae Hun, Shim Eun Bo
SiliconSapiens Inc., Seoul 06097, Korea.
Department of Physiology, University of Ulsan College of Medicine, Seoul 05505, Korea.
Korean J Physiol Pharmacol. 2019 Sep;23(5):305-310. doi: 10.4196/kjpp.2019.23.5.305. Epub 2019 Aug 26.
The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.
生理组学方法目前在心血管疾病诊断中得到广泛应用。心血管生理组学有两种可能的方法：传统数学模型和机器学习（ML）算法。ML在社会的几乎每个领域都有应用，用于以前由人类执行的各种任务。具体而言，心血管医学中的各种ML技术正以前所未有的速度得到发展和改进。将ML用于各种任务的好处在于无需了解系统的内部工作机制，这在确定系统内部工作机制可能很困难的情况下很方便。其计算速度通常也高于传统数学模型。ML的局限性在于它本质上会导致近似值，在需要高精度的情况下必须格外小心。然而，传统数学模型是基于已证明或假设的基本定律构建的。只要模型正确，数学模型的结果就是准确的。将数学模型和ML的优点结合起来，对于许多问题都将提高模拟的准确性和效率。在本综述中，介绍了数学建模方法和ML可以结合的心血管生理组学实例。
