Application of Artificial Intelligence in Cardiovascular Medicine

Cheng, Xi; Manandhar, Ishan; Aryal, Sachin; Joe, Bina

doi:10.1002/cphy.c200034

Cited by 6 publications

(14 citation statements)

References 152 publications

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“…Currently, ML has gradually become a novel research hotspot among auxiliary diagnosis and have subsequently captured the attention of the cardiovascular research field. An increasing number of studies have demonstrated that ML can help distinguish imaging pictures and electrocardiograms, suggesting the application of ML in the auxiliary diagnosis of CVD (2,3). Thus, we discuss the diagnostic values of ML in CVD.…”

Section: Application Of ML In the Diagnosis Of Cvdsmentioning

confidence: 97%

“…Cardiovascular disease (CVD) is one of the leading causes and is estimated to account for approximately 19 million deaths globally in 2021 (1). CVD can be divided into several types, including heart failure (HF), arrhythmia, coronary heart disease (CHD), hypertension, valvular heart disease and other diseases (2). Early diagnosis and assessment of CVD are crucial to reduce serious complications and deaths.…”

Section: Introductionmentioning

confidence: 99%

“…ML is mainly divided into supervised and unsupervised learning (2). Supervised learning refers to the establishment of mathematical models that use known samples of specific characteristics as training sets, and then map new unknown samples with established models (2). Thus, supervisedlearning models use input training sets that have labels and features (2).…”

Section: Introductionmentioning

confidence: 99%

“…Supervised learning refers to the establishment of mathematical models that use known samples of specific characteristics as training sets, and then map new unknown samples with established models (2). Thus, supervisedlearning models use input training sets that have labels and features (2). Commonly, supervised-learning models include logistic regression, decision tree, support vector machine (SVM), naive bayes, random forest (RF), and artificial neural network models (8,9).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Chen

Han

Wang³

et al. 2022

Ann Transl Med

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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.

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Section: Application Of ML In the Diagnosis Of Cvdsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Chen

Han

Wang³

et al. 2022

Ann Transl Med

View full text Add to dashboard Cite

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“…Combinations of methods or modification of separating hyperplanes, classification margins, boundaries, etc., can improve the generalization of SVM [ 30 ], avoiding the under-fitting and overfitting problems in previous attempts at neural network learning and yielding high generalization ability [ 31 ]. Decision tree (DT) is a basic classification and regression method in machine learning that mainly includes feature selection, decision tree generation, and pruning [ 32 , 33 ]. Common DT algorithm models include the ID3, C4.5, and CART algorithms [ 34 ].…”

Section: Machine Learning Algorithms-traditional Machine Learning Alg...mentioning

confidence: 99%

Machine learning in TCM with natural products and molecules: current status and future perspectives

Liu

et al. 2023

Chin Med

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Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.

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A review of disease risk prediction methods and applications in the omics era

Sun,

Cheng,

et al. 2024

Proteomics

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Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics‐based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

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Application of Artificial Intelligence in Cardiovascular Medicine

Cited by 6 publications

References 152 publications

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

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

Machine learning in TCM with natural products and molecules: current status and future perspectives

A review of disease risk prediction methods and applications in the omics era

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