Artificial intelligence and machine learning in cardiovascular health care
A Kilic - The Annals of thoracic surgery, 2020 - Elsevier
Background This review article provides an overview of artificial intelligence (AI) and
machine learning (ML) as it relates to cardiovascular health care. Methods An overview of …
machine learning (ML) as it relates to cardiovascular health care. Methods An overview of …
Current and future applications of artificial intelligence in coronary artery disease
Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated
with substantial economic burden on healthcare systems around the world. Coronary artery …
with substantial economic burden on healthcare systems around the world. Coronary artery …
Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality
Aims This study aimed to review the performance of machine learning (ML) methods
compared with conventional statistical models (CSMs) for predicting readmission and …
compared with conventional statistical models (CSMs) for predicting readmission and …
Prognostically relevant periprocedural myocardial injury and infarction associated with percutaneous coronary interventions: a Consensus Document of the ESC …
A substantial number of chronic coronary syndrome (CCS) patients undergoing
percutaneous coronary intervention (PCI) experience periprocedural myocardial injury or …
percutaneous coronary intervention (PCI) experience periprocedural myocardial injury or …
Develo** window behavior models for residential buildings using XGBoost algorithm
Buildings account for over 32% of total society energy consumption, and to make buildings
more energy efficient dynamic building performance simulation has been widely adopted …
more energy efficient dynamic building performance simulation has been widely adopted …
Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study
N Al-Dury, A Ravn-Fischer, J Hollenberg… - Scandinavian journal of …, 2020 - Springer
Introduction Studies examining the factors linked to survival after out of hospital cardiac
arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in …
arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in …
The 12-lead electrocardiogram as a biomarker of biological age
AO Ladejobi, JR Medina-Inojosa… - … Heart Journal-Digital …, 2021 - academic.oup.com
Background We have demonstrated that a neural network is able to predict a person's age
from the electrocardiogram (ECG)[artificial intelligence (AI) ECG age]. However, some …
from the electrocardiogram (ECG)[artificial intelligence (AI) ECG age]. However, some …
Performance metrics for the comparative analysis of clinical risk prediction models employing machine learning
Background: New methods such as machine learning techniques have been increasingly
used to enhance the performance of risk predictions for clinical decision-making. However …
used to enhance the performance of risk predictions for clinical decision-making. However …
Machine learning for prediction and risk stratification of lupus nephritis renal flare
Y Chen, S Huang, T Chen, D Liang, J Yang… - American Journal of …, 2021 - karger.com
Background: Renal flare of lupus nephritis (LN) is strongly associated with poor kidney
outcomes, and predicting renal flare and stratifying its risk are important for clinical decision …
outcomes, and predicting renal flare and stratifying its risk are important for clinical decision …
Machine learning improves prediction over logistic regression on resected colon cancer patients
Introduction Despite advances, readmission and mortality rates for surgical patients with
colon cancer remain high. Prediction models using regression techniques allows for risk …
colon cancer remain high. Prediction models using regression techniques allows for risk …