ReviewJournal of the American Heart Association2025
Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.
Review in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cardiovascular risk-prediction models in cancer survivors: a meta-analysis.European heart journal · 2026Pooled it
- Review
- From Sensor-Empowered Ubiquitous Computing to Embodied Intelligence: Architectures, Paradigm Evolution, and Emerging Challenges.Sensors (Basel, Switzerland) · 2026Review
- Real-World Pharmacotherapy-Driven Cardiovascular Risk Prediction Using Interpretable Machine Learning and Jordanian EHR Data.Medical sciences (Basel, Switzerland) · 2026Article
- Machine Learning for Coronary Heart Disease Prediction: Comparative Analysis of Framingham and Cleveland Subset of the UCI Dataset with SHAP-Based Interpretability.Epidemiologia (Basel, Switzerland) · 2026Article
- Bridging the Gap: Multidisciplinary Decision Making to Address Systemic Barriers in Cardiovascular Care.Current cardiology reports · 2026Review
- Multi-modal federated learning with differential privacy for privacy-preserving healthcare AI.Scientific reports · 2026Article
- Aortic and Cardiac Structure From Routine CT Predict Cardiovascular Risk Beyond PREVENT and Coronary Calcium.JACC. Cardiovascular imaging · 2026Article
- Development and Validation of a Prognostic Nomogram for Post-Transcatheter Aortic Valve Replacement Heart Failure Hospitalization in Patients With Concurrent Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction: A Multicenter Study.Journal of the American Heart Association · 2026Article
- Twin Transformation in Cardiothoracic Surgery: The Convergence of Digital Innovation and Sustainability.Journal of cardiovascular development and disease · 2026Review
- Medical haematology: Repositioning haematology at the centre of medicine.British journal of haematology · 2026Review
- Review
- Precision immunopharmacology in peri-implantitis management: from molecular mechanisms to advanced therapeutic strategies.Frontiers in immunology · 2026Review
- Machine Learning for Early Detection and Prevention of Disease Using Electronic Health Records.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Quantifying Metabolic Syndrome Severity: Methodological Evolution, Clinical Validation, and Translational Perspectives.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
- Adoption of Machine Learning in US Hospital Electronic Health Record Systems: Retrospective Observational Study.Journal of medical Internet research · 2025Observational
- Biodata-centric cardiovascular disease prediction using multi-objective genetic algorithm-driven deep ensembles.Scientific reports · 2025Article
- AI approaches for predicting progression to acute coronary syndrome among stable coronary heart disease patients.NPJ cardiovascular health · 2025Article
- Decoding long COVID-associated cardiovascular dysfunction: Mechanisms, models, and new approach methodologies.Journal of molecular and cellular cardiology · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electronic health records (EHR) have revolutionized cardiovascular disease (CVD) research by enabling comprehensive, large-scale, and dynamic data collection. Integrating EHR data with advanced analytical methods, including artificial intelligence (AI), transforms CVD risk prediction and management methodologies. This review examines the advancements and challenges of using EHR in developing CVD prediction models, covering traditional and AI-based approaches. While EHR-based CVD risk prediction has greatly improved, moving from models that integrate real-world data on medication use and imaging, challenges persist regarding data quality, standardization across health care systems, and geographic variability. The complexity of EHR data requires sophisticated computational methods and multidisciplinary approaches for effective CVD risk modeling. AI's deep learning enhances prediction performance but faces limitations in interpretability and the need for validation and recalibration for diverse populations. The future of CVD risk prediction and management increasingly depends on using EHR and AI technologies effectively. Addressing data quality issues and overcoming limitations from retrospective data analysis are critical for improving the reliability and applicability of risk prediction models. Integrating multidimensional data, including environmental, lifestyle, social, and genomic factors, could significantly enhance risk assessment. These models require continuous validation and recalibration to ensure their adaptability to diverse populations and evolving health care environments, providing reassurance about their reliability.
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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 OpenQuestion graph.