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Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Open accessAbstract
The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart 1. 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.
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“Emerging evidence suggests that ECG waveforms contain latent signatures of ventricular dysfunction [4], structural heart disease [5], metabolic abnormalities [6], and broader physiologic characteristics such as biological age [7] and future mortality risk [8].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [8]
In the citing paper
“Cardiologists reviewing selected normal ECGs were unable to reliably identify the mortality-associated patterns detected by the model [8].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [8]
In the citing paper
“maintained strong performance even among ECGs interpreted as normal by physicians, with an AUC of 0.85.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [8]
In the citing paper
“As discussed above, ECG-age [7], mortality-prediction [8], and disease-specific models illustrate how AI-derived outputs may function as quantitative markers of physiologic risk rather than simple diagnostic labels.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [8]
In the citing paper
“Raghunath et al. trained a deep neural network using more than 1.1 million 12-lead ECGs from over 250,000 patients to predict one-year all-cause mortality.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [8]
In the citing paper
“This study demonstrated that AI-ECG can extract prognostic information from ECG waveforms that may not be visually apparent”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [8]
In the citing paper
“The model achieved an AUC of 0.88 in the held-out test set”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [8]
In the citing paper
“Contemporary examples of these clinical applications in cardiology include the use of ECGs to predict left ventricular dysfunction or future development of atrial fibrillation (AF).[13][14][15][16]”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [14]
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Metadata
- Authors
- 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., Nemani, A., Carbonati, T., Johnson, K. W., Young, K., Good, C. W., Pfeifer, J. M., Patel, A. A., Delisle, B. P., Alsaid, A., Beer, D., Haggerty, C. M., & Fornwalt, B. K.
- Affiliations
- Bucknell University (US)
- Geisinger Medical Center (US)
- Tempus Labs (United States) (US)
- University of Kentucky (US)
- Corresponding author
- Fornwalt, Brandon K.
- ORCID
- Venue
- Nature Medicine
- Published
- 2020-05-11
- Volume
- 26
- Issue
- 6
- Pages
- 886-891
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W3104075323
- Cited by
- 314
- References
- 59
- Citations by year
- 2026: 41 · 2025: 49 · 2024: 61 · 2023: 51 · 2022: 53 · 2021: 49 · 2020: 9
- FWCI
- 25.00
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Nature Medicine (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Geisinger Health System Foundation
- Geisinger Health Plan Tempus Labs Inc
- Pennsylvania Department of Health (SAP 4100070267)
- American Heart Association (17CCRG33700289)
- Tempus Labs Inc
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- 12-lead ECG, all-cause mortality prediction, deep neural network, resting ECG, prognostic stratification, Cox proportional hazards model, cardiovascular risk prediction
- MeSH terms
- Cardiologists, Deep Learning, Adult, Aged, Aged, 80 and over, Algorithms, Cause of Death, Electrocardiography, Female, Humans, Male, Middle Aged, Mortality, Prognosis, Retrospective Studies, ROC Curve, Proportional Hazards Models, Neural Networks, Computer, Risk Assessment, Area Under Curve
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