| Title: | Building a prediction and early warning system of cardiovascular disease based on artificial intelligence |
| Lead Institution: | University of Electronic Science and Technology of China |
| Principal investigator: |
Application 106027
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About
Cardiovascular disease(CVD), the number one cause of death worldwide,is a group of heart and blood vessel diseases that includes coronary heart disease, cardiomyopathy, arrhythmia, valvular heart disease and congenital heart disease. In order to obtain the genetic subtypes of CVD with clinical intervention significance, reduce the mortality of CVd and effectively prevent sudden death, in this study, biobank data and data information of CVD patients in hospitals were used to screen candidate pathogenic genes for CVD, and gene mutation data combined with multimodal data were used to classify gene subtypes of CVD and provide clinical significance, multi-center data were used to verify the validity of the prediction model for guiding clinical practice. Through various methods such as machine learning and deep learning of artificial intelligence, the prediction model and early warning system of death and sudden death of CVD are constructed to identify high-risk patients with clinical death and sudden death, and the fusion model containing pathogenic gene and mutation information is constructed to further improve the accuracy of the model. This study is helpful for clinical identification of high-risk patients, timely medical intervention and intensive treatment to prevent sudden death, thereby reducing the risk of death and improving the prognosis of patients.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 16363 | Association of metabolism score for visceral fat with new-onset cardiovascular disease in patients with metabolic syndrome: two large prospective cohorts in Europe and Asia | Zhen Tan (+7) | 2025 |
Enabling scientific discoveries that improve human health