Pathways leading to prevention of fatal and non-fatal cardiovascular disease: An interaction model on 15 years population-based cohort study

Shakibaei, Najmeh; Hassannejad, Razieh; Mohammadifard, Noushin; Marateb, Hamid Reza; Mansourian, Marjan; Mañanas, Miguel Ángel; Sarrafzadegan, Nizal

doi:10.1186/s12944-020-01375-8

Cited by 3 publications

(1 citation statement)

References 59 publications

(77 reference statements)

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“…Unsupervised learning, on the other hand, analyzes large amounts of typically unlabeled samples (e.g., EHR) to discover hidden patterns or innate structure which govern the existence of that data in order to substantially improve experts' understanding of that data including their involved representing features ( 28 ). In cardiology, for example, it has been shown that advanced unsupervised models such as causal networks can evaluate causal relationships among variables beyond partial correlations and thus play a fundamental step in risk prediction of cardiovascular disease (CVD) ( 29 ).…”

Section: Machine and Deep Learning Overviewmentioning

confidence: 99%

Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

Ali

Pesaranghader

Avram

et al. 2021

Front. Cardiovasc. Med.

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Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.

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Section: Machine and Deep Learning Overviewmentioning

confidence: 99%