The emerging roles of machine learning in cardiovascular diseases: a narrative review

Abstract

Background and Objective: With the wide application of electronic medical record systems in hospitals, massive medical data are available. This type of medical data has the characteristics of heterogeneity and multi-dimensionality. Traditional statistical methods cannot fully extract and use such data, but with their non-linear and cross-learning modes, machine-learning (ML) algorithms based on artificial intelligence can address these shortcomings. To explore the application of ML algorithms in the cardiovascular field, we retrieved and reviewed relevant articles published in the last 6 years and found that ML is practical and accurate in the auxiliary diagnosis of cardiovascular diseases. Thus, this article reviewed the research progress of ML in cardiovascular disease. Methods: This study searched relevant literature published in National Center for Biotechnology Information (NCBI) PubMed from 2016 to 2022. The relevant literature was extracted from NCBI PubMed with the following keywords and their combinations: "machine learning", "artificial intelligence", "cardiology", "cardiovascular disease", "echocardiography", "electrocardiogram" and "prediction model". All articles included in the review are English. Key Content and Findings: The review found that ML is practical and accurate in the diagnosis of cardiovascular diseases. Besides, ML can build clinical risk prediction models and help doctors evaluate the prognosis of patients. Conclusions: The study summarized the progress of ML in cardiovascular diseases and confirmed its advantages in clinical application. In the future, models and software based on ML will be common auxiliary tools in clinical practice.

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