Acute myocardial infarction (AMI), as the leading cause of death among cardiovascular diseases, has its diagnosis and treatment efficiency directly affecting survival. Although the current diagnosis and treatment system has significantly improved in-hospital outcomes, delays in seeking medical care due to patients' insufficient awareness and out-of-hospital deaths are common, representing the biggest bottleneck in improving diagnostic and treatment capabilities. This study takes intelligent-assisted diagnosis of AMI as the entry point and proposes a technical approach that combines a deep learning algorithm based on 12-lead electrocardiograms with wearable monitoring devices. By utilizing morphological feature extraction and deep learning models, it aims to achieve early identification and warning of AMI. The study plans to build a multi-center AMI long-term follow-up cohort covering the Beijing area based on spatiotemporal heterogeneous data. By integrating and forming a precise high-risk cohort of 3,000 acute myocardial infarction cases, it seeks to construct an AMI risk prediction model that combines deep learning with a retrieval-augmented generative expert system, breaking through bottlenecks in ECG recognition and temporal prediction, enhancing model generalization and transferability. Ultimately, it will support the application of wearable devices, shorten pre-hospital delays, achieve early warning and precise diagnosis of AMI, reduce reinfarction and cardiac-related mortality, and carry significant clinical and public health importance.
Study Type
OBSERVATIONAL
Enrollment
3,000
For high-risk patients with myocardial infarction, screen and integrate relevant previous prospective, multicenter AMI cohorts, and establish a multicenter, multi-treatment precise high-risk acute myocardial infarction cohort in the Beijing region, with long-term follow-up and supplementation of multidimensional data. Subsequently, based on semantic knowledge-guided cross-modal and cross-timepoint data alignment, use domain adaptation methods to perform fusion modeling of spatiotemporal and modal heterogeneous AMI cohorts. Through multimodal interpretable artificial intelligence models, mine the fused models to complete the construction and validation of an AMI risk prediction model based on spatiotemporally heterogeneous data.
The time of the first occurrence of acute myocardial infarction (AMI) within 1 year after enrollment.
Time frame: 1 year
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.