This data was used to develop a machine learning (ML)-based real-time predictive model for sudden cardiac arrest predictions in critical care settings.

In this study, we developed and evaluated a DL-based model that has a feature extraction stage, an ECG-lead subset selection stage and a decision-making stage to automatically interpret multiple ...

Machine Learning ‘Sniffs’ Out Long QT Otherwise Unseen on ECG This tool, which mines ECG data beyond what the clinician can see, will likely ease the identification of other cardiac conditions.

Study: Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Image Credit: vchal / Shutterstock.com Challenges in the timely diagnosis of OMI ST is a ...

Machine-learning models that incorporate common clinical features along with ECG findings may help identify patients who are likely to have coronary artery calcium (CAC), with potential implications ...

The time-tested technique for predicting numbers, and the role of domain knowledge in machine learning.

Background Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.Methods From the SCOT-HEART ...