A deep learning phenome wide association study of the electrocardiogram.

ABSTRACT:

Aims

Deep learning methods have shown impressive performance in detecting a range of diseases from electrocardiogram (ECG) waveforms, but the breadth of diseases that can be detected with high accuracy remains unknown, and in many cases the changes to the ECG allowing these classifications are also opaque. In this study, we aim to determine the full set of cardiac and non-cardiac conditions detectable from the ECG and to understand which ECG features contribute to the disease classification.

Methods and results

Using large datasets of ECGs and connected electronic health records from two separate medical centres, we independently trained PheWASNet, a multi-task deep learning model, to detect 1243 different disease phenotypes from the raw ECG waveform. We confirmed that the E

SUBMITTER: Hughes JW 

PROVIDER: S-EPMC12282379 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature