ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram.

ABSTRACT:

Background

Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling.

Methods

This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-sur

SUBMITTER: Lin CH 

PROVIDER: S-EPMC11751416 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature