Toward a grey box approach for cardiovascular physiome

• 발행기관: 대한생리학회-대한약리학회
• 저자명: Minki Hwang Chae Hun Leem Eun Bo Shim
• 간행물 정보: 『The Korean Journal of Physiology & Pharmacology』제23권 제5호, 305~310쪽, 전체 6쪽
• 주제분류: 의약학 > 의학일반
• 파일형태: PDF
• 발행일자: 2019.09.30

영문 초록

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.

목차

MACHINE LEARNING METHODOLOGIES

APPLICATIONS OF MACHINE LEARNING TO PHYSIOMIC SIMULATIONS

GREY BOX APPROACH

CONCLUSION

키워드

Machine learning Mathematical model Patient-specific modeling