Jing, Linyuan

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network

Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., et al. (2020) · Nature Medicine

ECG Monitoring and AnalysisMachine Learning in Healthcare

Cited by 314

Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation-related stroke

Raghunath, S., Pfeifer, J. M., Ulloa-Cerna, A. E., Nemani, A., Carbonati, T., Jing, L., vanMaanen, D. P., Hartzel, D. N., Ruhl, J. A., Lagerman, B. F., et al. (2021) · Circulation

ECG Monitoring and AnalysisAtrial Fibrillation Management and Outcomes

Cited by 312

Detecting structural heart disease from electrocardiograms using AI

Poterucha, T. J., Jing, L., Ricart, R. P., Adjei-Mosi, M., Finer, J., Hartzel, D., Kelsey, C., Long, A., Rocha, D., Ruhl, J. A., et al. (2025) · Nature

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare and Education

Cited by 116

rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography

Ulloa-Cerna, A. E., Jing, L., Pfeifer, J. M., Raghunath, S., Ruhl, J. A., Rocha, D. B., Leader, J. B., Zimmerman, N., Lee, G., Steinhubl, S. R., et al. (2022) · Circulation

Artificial Intelligence in HealthcareECG Monitoring and Analysis

Cited by 101