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Antonio Horta Ribeiro

Assistant Professor

Uppsala University

Sweden

Research Interests

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Antonio Horta Ribeiro is an Assistant Professor at Uppsala University in Sweden. His research primarily focuses on electrocardiography, deep learning applications in cardiovascular health, and the genetic underpinnings of cardiac conditions. Recent articles co-authored by him explore the prediction and significance of atrial fibrillation, the application of artificial intelligence in electrocardiographic assessments, and the association of neural network-derived features with cardiovascular risk.

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Collaborators (22)

Honghuang Lin

Professor

UMass Medical School

UNITED STATES

Antonio Horta Ribeiro

Research Interests

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Articles (28)

On the alignment between chemical foundation models and neural processing of olfactory perception

A multi-source domain fine-tuning framework for deep generalization performance in physiological time series analysis

A deep learning ECG model for identification and localization of occlusion myocardial infarction

Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals

A CNN-Based Local-Global Self-attention via Averaged Window Embeddings for Hierarchical ECG Analysis

AHA/ACC/ESC/WHF Expert Consensus Document: Second Universal Definition of Heart Failure (2026)

Explaining deep learning for ECG using time-localized clusters

Tele-Electrocardiography and Mortality: Clinical Outcomes in Digital Electrocardiography Cohort- Data from Belo Horizonte, Brazil (CODE-BH)

Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study

Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study

AI-ECG and prediction of new atrial fibrillation: when the heart tells the age

A comparison of artificial intelligence-enhanced electrocardiography approaches for the prediction of time to mortality using electrocardiogram images

Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study

Contrasting by Augmented Patient Electrocardiograms to Learn Representations for a Foundation Model

Deep networks for system identification: A survey

Evaluation of AI ECG age in the prediction of cardiovascular diseases and risk factors: Exploratory data analysis

Generalization challenges in electrocardiogram deep learning: insights from dataset characteristics and attention mechanism

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features

Genetic and phenotypic architecture of human myocardial trabeculation

Association Between Electrocardiographic Age and Cardiovascular Events in Community Settings: The Framingham Heart Study

Invertible Kernel PCA With Random Fourier Features

Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images

On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG

Association of lifestyle with deep learning predicted electrocardiographic age

End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks

Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival

Neural network-derived electrocardiographic features have prognostic significance and important phenotypic and genotypic associations

Collaborators (22)

Honghuang Lin

Todd Schlegel

Bobak J. Mortazavi

Jessica Fetterman

Konstantinos Patlatzoglou

Emelia Benjamin

Guido Gagliardi

Thomas Schön

James Ware

Nikesh Bajaj

Sarah Preis

Jared W. Magnani

Mariya Toneva

Fu Siong Ng

Dagmar Krefting

Nhan Truong

Danica Kragic

Nona Rajabi

Leping Yu

Omid Kavehei

Johan Lundström

Nicolai Spicher

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