ArticleNPJ digital medicine2023
Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Artificial Intelligence Applied to Electrocardiogram-Based Cardiovascular Risk Assessment: A Systematic Review.Arquivos brasileiros de cardiologia · 2026Pooled it
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Artificial Intelligence-Enabled Serial Electrocardiograms for Prediction of All-Cause Mortality in Secondary Care Settings.JACC. Advances · 2026Article
- Multimodal Transformer-Based Electrocardiogram Analysis for Cardiovascular Comorbidity Detection: Model Development and Validation Study.JMIR formative research · 2026Article
- Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study.BioData mining · 2025Article
- Digitization and Linkage of PDF Formatted 12-Lead Electrocardiograms in Adult Congenital Heart Disease.CJC pediatric and congenital heart disease · 2025Article
- Risk stratification of chest pain in the emergency department using artificial intelligence applied to electrocardiograms.Open heart · 2025Article
- Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study.NPJ digital medicine · 2025Article
- A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction.NPJ digital medicine · 2025Article
- Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease.NPJ digital medicine · 2024Article
- Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level.NPJ digital medicine · 2024Article
- Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions.Archives of academic emergency medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The feasibility and value of linking electrocardiogram (ECG) data to longitudinal population-level administrative health data to facilitate the development of a learning healthcare system has not been fully explored. We developed ECG-based machine learning models to predict risk of mortality among patients presenting to an emergency department or hospital for any reason. Using the 12-lead ECG traces and measurements from 1,605,268 ECGs from 748,773 healthcare episodes of 244,077 patients (2007-2020) in Alberta, Canada, we developed and validated ResNet-based Deep Learning (DL) and gradient boosting-based XGBoost (XGB) models to predict 30-day, 1-year, and 5-year mortality. The models for 30-day, 1-year, and 5-year mortality were trained on 146,173, 141,072, and 111,020 patients and evaluated on 97,144, 89,379, and 55,650 patients, respectively. In the evaluation cohort, 7.6%, 17.3%, and 32.9% patients died by 30-days, 1-year, and 5-years, respectively. ResNet models based on ECG traces alone had good-to-excellent performance with area under receiver operating characteristic curve (AUROC) of 0.843 (95% CI: 0.838-0.848), 0.812 (0.808-0.816), and 0.798 (0.792-0.803) for 30-day, 1-year and 5-year prediction, respectively; and were superior to XGB models based on ECG measurements with AUROC of 0.782 (0.776-0.789), 0.784 (0.780-0.788), and 0.746 (0.740-0.751). This study demonstrates the validity of ECG-based DL mortality prediction models at the population-level that can be leveraged for prognostication at point of care.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.