Atrial fibrillation (AF) is a common cardiac arrhythmia characterized by rapid and irregular contractions of the atria. It significantly elevates the risk of strokes due to slowed blood flow in the atria, especially in the left atrial appendage, which is prone to blood clot formation. Such clots can migrate into cerebral arteries, leading to ischemic stroke. To assess whether AF patients should be prescribed anticoagulants, doctors often use the CHA2DS2-VASc scoring system. However, anticoagulant use must be approached with caution as it can impact clotting functions. This study introduces a machine learning algorithm that predicts whether patients with AF should be recommended anticoagulant therapy using 12-lead ECG data. In this model, we use STOME to enhance time-series data and then process it through a Convolutional Neural Network (CNN). By incorporating a path development layer, the model achieves a specificity of 30.6% under the condition of an NPV of 1. In contrast, LSTM algorithms without path development yield a specificity of only 2.7% under the same NPV condition.

通过使用12导联心电图数据，本研究引入了一种机器学习算法，用于预测是否应该向房颤患者推荐使用抗凝治疗。该算法利用STOME增强时间序列数据，并通过卷积神经网络进行处理。通过引入一种路径发展层，该模型在阴性预测率为1的条件下实现了30.6%的特异性，而没有引入路径发展的LSTM算法在相同条件下仅有2.7%的特异性。

通过路径发展网络优化心房颤动患者的药物决策