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

Sushravya Raghunath, Alvaro E. Ulloa Cerna, Linyuan Jing, David P. vanMaanen, Joshua Stough, Dustin N. Hartzel, Joseph B. Leader, H. Lester Kirchner, Martin C. Stumpe, Ashraf Hafez, Arun Nemani, Tanner Carbonati, Kipp W. Johnson, Katelyn Young, Christopher W. Good, John M. Pfeifer, Aalpen A. Patel, Brian P. Delisle, Amro Alsaid, Dominik BeerChristopher M. Haggerty, Brandon K. Fornwalt

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart1. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage–time traces. By using ECGs collected over a 34-year period in a large regional health system, we trained a DNN with 1,169,662 12-lead resting ECGs obtained from 253,397 patients, in which 99,371 events occurred. The model achieved an area under the curve (AUC) of 0.88 on a held-out test set of 168,914 patients, in which 14,207 events occurred. Even within the large subset of patients (n = 45,285) with ECGs interpreted as ‘normal’ by a physician, the performance of the model in predicting 1-year mortality remained high (AUC = 0.85). A blinded survey of cardiologists demonstrated that many of the discriminating features of these normal ECGs were not apparent to expert reviewers. Finally, a Cox proportional-hazard model revealed a hazard ratio of 9.5 (P < 0.005) for the two predicted groups (dead versus alive 1 year after ECG) over a 25-year follow-up period. These results show that deep learning can add substantial prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as normal by physicians.

Original languageEnglish
Pages (from-to)886-891
Number of pages6
JournalNature Medicine
Issue number6
StatePublished - Jun 1 2020

Bibliographical note

Funding Information:
This work was supported in part by funding from the Pennsylvania Department of Health (SAP 4100070267), an American Heart Association Competitive Catalyst Award (17CCRG33700289), the Geisinger Health Plan and Clinic, and Tempus. The content of this article does not reflect the views of the funding sources. Geisinger receives funding from Tempus for ongoing development of predictive modeling technology and commercialization. Tempus and Geisinger have jointly applied for a patent related to the work. None of the Geisinger authors has ownership interest in any of the intellectual property resulting from the partnership. A.N., T.C., A.H., M.C.S. and K.W.J. are employees of Tempus.

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (all)


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