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Multi-modal fusion model for predicting adverse cardiovascular outcome post percutaneous coronary intervention
Conclusion. To the best of our knowledge, this is the first study that developed a deep learning model with joint fusion architecture for the prediction of post-PCI prognosis and outperformed machine learning models developed using traditional single-source features (clinical variables or E CG features). Adding ECG data with clinical variables did not improve prediction of all-cause mortality as may be expected, but the improved performance of related cardiac outcomes shows that the fusion of ECG generates additional value.
Source: Physiological Measurement - December 22, 2022 Category: Physiology Authors: Amartya Bhattacharya, Sudarsan Sadasivuni, Chieh-Ju Chao, Pradyumna Agasthi, Chadi Ayoub, David R Holmes, Reza Arsanjani, Arindam Sanyal and Imon Banerjee Source Type: research