EMBL-EBI scientists and collaborators at Heidelberg University have developed CORNETO, a new computational tool that uses machine learning to gain meaningful insights from complex biological data.

Researchers developed an AI-powered ECG model, EchoNext, that detects structural heart disease with high accuracy across ...

NewYork-Presbyterian researchers have developed a deep learning model that increases doctors' abilities to diagnose ...

We compiled these methods and placed them into a python class that can be leveraged by others to help drive research forward. Compared with traditional machine learning methods, our results show that ...

ECG-based machine learning offers a promising, interpretable approach for liver disease detection, particularly in resource-limited settings. By revealing clinically relevant biomarkers, this method ...

The presented machine learning model based on serial ECGs with normal sinus rhythm can predict new‐onset atrial fibrillation more accurately than a machine learning model based on a single ECG.

We hypothesized that analysis of serial ECGs could predict new‐onset atrial fibrillation (AF) more accurately than analysis of a single ECG by detecting the subtle cardiac remodeling that occurs ...

Background We hypothesized that analysis of serial ECGs could predict new‐onset atrial fibrillation (AF) more accurately than analysis of a single ECG by detecting the subtle cardiac remodeling that ...

It is challenging to learn machine learning. For me, great examples for common workflows are crtical. So I built out over 20 well-documented demonstration workflows that apply machine learning to ...

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