pISSN 1226-4512 eISSN 2093-3827

Article

home Article View

Review Article

Korean J Physiol Pharmacol 2019; 23(5): 305-310

Published online September 1, 2019 https://doi.org/10.4196/kjpp.2019.23.5.305

Copyright © The Korean Journal of Physiology & Pharmacology.

Toward a grey box approach for cardiovascular physiome

Minki Hwang1, Chae Hun Leem2, and Eun Bo Shim1,3,*

1SiliconSapiens Inc., Seoul 06097, 2Department of Physiology, University of Ulsan College of Medicine, Seoul 05505, 3Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon 24341, Korea

Correspondence to:*Eun Bo Shim, E-mail: ebshim@kangwon.ac.kr

Received: July 10, 2019; Revised: August 2, 2019; Accepted: August 6, 2019

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.

Keywords: Machine learning, Mathematical model, Patient-specific modeling

Stats or Metrics

Share this article on :

Related articles in KJPP