ML for MI - Integrating Multimodal Information in Machine Learning for Predicting Acute Myocardial Infarction

Abstract

AbstractEarly identification and recognization of myocardial ischemia/infarction (MI) is the most important goal in the management of acute coronary syndrome (ACS). The 12-lead electrocardiogram (ECG) is widely used as the initial screening test for patients with chest pain but its diagnostic accuracy remains limited. There is an ongoing effort to address the issue with machine learning (ML) algorithms which have demonstrated improved performance. Most studies are designed to classify MI from healthy controls and thus are limited due to the lack of consideration of potential confounding conditions in the setting of MI. Moreover, other clinical information in addition to ECG has not yet been well leveraged in existing machine learning models. The present study aims to advance ML-based prediction models closer to clinical applications for early MI detection. The study considered downstream clinical implementation scenarios in the initial model design by dichotomizing study samples into MI and non-MI classes. Two separate experiments were then conducted to systematically investigate the impact of two important factors entrained in the modeling process, including the duration of ECG (2.5s vs. 10s), and the value of multimodal information for model training. A novel feature-fusion deep learning architecture was proposed to learn joint features from both ECG and patient demographics as the additional data modality. The best-performing model achieved a mean area under the receiver operating characteristic curve (AUROC) of 92.1% and a mean accuracy of 87.4%, which is on par with existing studies despite the increased task difficulty due to the new class design. The results also show that the ML model can capitalize on the information added from both the extra ECG waveforms in time and patient demographics. The findings in this study help guide the development of machine learning solutions for early MI detection and move the models one step closer to real-world clinical applications.