This study introduces a machine learning algorithm that predicts whether patients with Atrial fibrillation (AF) should be recommended anticoagulant therapy using 12-lead ECG data. The model uses STOME to enhance time-series data and then processes it through a Convolutional Neural Network (CNN). By incorporating a path development layer, the model achieves better specificity. This research has the potential to significantly enhance the treatment methodology for patients with AF, reducing reliance on anticoagulants, thereby decreasing the associated health risks and medical costs.
Publication date: 19 Jan 2024
Project Page: https://arxiv.org/abs/2401.10014
Paper: https://arxiv.org/pdf/2401.10014
