Cross-Modal Deep Learning Based on Texts and ECG Images for Risk Prediction of Patients with Acute Chest Pain in the Emergency Department

Abstract

Acute chest pain is one of the most common complaints and is frequently related to life-threatening diseases in the emergency department. We aimed to construct a cross-modal deep learning model for risk prediction of acute chest pain by the physicians' clinical texts and electrocardiogram (ECG). Two different modalities included the initial ECG image and the physicians' notes are used to predict the disposition.