Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …
tools in medicine and healthcare. Deep learning methods have achieved promising results …
Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure
Abstract Machine learning and artificial intelligence are generating significant attention in
the scientific community and media. Such algorithms have great potential in medicine for …
the scientific community and media. Such algorithms have great potential in medicine for …
Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: proceedings of the European Society of Cardiology Cardiovascular Round Table
C Leclercq, H Witt, G Hindricks, RP Katra, D Albert… - Europace, 2022 - academic.oup.com
Digital technology is now an integral part of medicine. Tools for detecting, screening,
diagnosis, and monitoring health-related parameters have improved patient care and …
diagnosis, and monitoring health-related parameters have improved patient care and …
Comparing different machine learning techniques for predicting COVID-19 severity
Y **ong, Y Ma, L Ruan, D Li, C Lu, L Huang… - Infectious diseases of …, 2022 - Springer
Abstract Background Coronavirus disease 2019 (COVID-19) is still ongoing spreading
globally, machine learning techniques were used in disease diagnosis and to predict …
globally, machine learning techniques were used in disease diagnosis and to predict …
Artificial intelligence and heart failure: A state‐of‐the‐art review
Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals
globally. Despite recent advancements in understanding of the pathophysiology of HF, many …
globally. Despite recent advancements in understanding of the pathophysiology of HF, many …
Applications of artificial intelligence and machine learning in heart failure
T Averbuch, K Sullivan, A Sauer… - … Heart Journal-Digital …, 2022 - academic.oup.com
Abstract Machine learning (ML) is a sub-field of artificial intelligence that uses computer
algorithms to extract patterns from raw data, acquire knowledge without human input, and …
algorithms to extract patterns from raw data, acquire knowledge without human input, and …
Machine learning–based models incorporating social determinants of health vs traditional models for predicting in-hospital mortality in patients with heart failure
Importance Traditional models for predicting in-hospital mortality for patients with heart
failure (HF) have used logistic regression and do not account for social determinants of …
failure (HF) have used logistic regression and do not account for social determinants of …
Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments
Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic …
(SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic …
Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review
Abstract Machine learning (ML) algorithms “learn” information directly from data, and their
performance improves proportionally with the number of high-quality samples. The aim of …
performance improves proportionally with the number of high-quality samples. The aim of …
Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical …
Background Machine learning (ML) is increasingly used in research for subtype definition
and risk prediction, particularly in cardiovascular diseases. No existing ML models are …
and risk prediction, particularly in cardiovascular diseases. No existing ML models are …