Improving ECG-based COVID-19 Diagnosis and Mortality Predictions Using Pre-pandemic Medical Records at Population-scale
Published in NeurIPS 2022 Workshop on Learning from Time Series for Health, 2022
This research explores the potential of leveraging pre-pandemic medical records to refine the accuracy of ECG-based diagnostics and mortality predictions for COVID-19. By employing a comprehensive dataset and advanced machine learning techniques, the study presents a groundbreaking approach to enhancing pandemic response strategies through predictive health analytics.
Recommended citation: Sun, W., Kalmady, S., Sepehrvand, N., Chu, L., Wang, Z., Salimi, A., Hindle, A., Greiner, R., & Kaul, P. (2022). "Improving ECG-based COVID-19 Diagnosis and Mortality Predictions Using Pre-pandemic Medical Records at Population-scale." NeurIPS 2022 Workshop on Learning from Time Series for Health. [PDF]