ReviewFrontiers in cardiovascular medicine2022
Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care.
Review in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses 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
47 citing papers in PubMed, 3 syntheses or guidelines pooled it, 142 citations in OpenAlex.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Opportunities and Challenges of Cardiovascular Disease Risk Prediction for Primary Prevention Using Machine Learning and Electronic Health Records: A Systematic Review.Reviews in cardiovascular medicine · 2025Pooled it
- Predicting occupational injury causal factors using text-based analytics: A systematic review.Frontiers in public health · 2022Pooled it
- Interaction of artificial intelligence, mental disorders, and diverse data modalities: Potential treatment management based on the "method-disease-data" axis.Neural regeneration research · 2026Article
- An Explainable Quality-Aware ECG-PCG Fusion for Cardiovascular Disease Detection Using Robust Feature Modeling.Diagnostics (Basel, Switzerland) · 2026Article
- Cross-modal attentive fusion network for tri-modal lesion growth prediction.Scientific reports · 2026Article
- Importance of balanced datasets with feature selection and ensemble methods on heart disease classification using distinctive machine learning techniques: a comparative analysis.Scientific reports · 2026Article
- Fully automated, deep learning, cardiac CT-based multimodal network for cardiovascular risk stratification in high-risk perioperative patients.European heart journal. Digital health · 2026Article
- Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support.Medical sciences (Basel, Switzerland) · 2026Review
- Integrative multimodal hybrid data fusion for mortality prediction.Scientific reports · 2026Article
- Multimodal fusion of EHR and ECG based on deep learning for predicting new-onset coronary heart disease in cancer patients.Frontiers in cardiovascular medicine · 2026Article
- A systematic review of multimodal machine learning models for heart failure classification and prognosis prediction.Frontiers in cardiovascular medicine · 2026Review
- Intelligent data governance and quality control for chest Bi syndrome/coronary heart disease across the prevention-treatment-rehabilitation continuum: integrating a standardized framework, adversarially-optimized trigger engine, and domain-adaptive AI.Frontiers in medical technology · 2026Article
- Pooling multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal.Bioinformatics advances · 2026Article
- A Narrative Review of Multimodal Data Fusion Strategies for Precision Risk Prediction in Coronary Artery Disease: Advances, Challenges, and Future Informatics Directions.Rambam Maimonides medical journal · 2025Review
- Multimodal Integration in Health Care: Development With Applications in Disease Management.Journal of medical Internet research · 2025Review
- Hybrid CNN-Transformer-WOA model with XGBoost-SHAP feature selection for arrhythmia risk prediction in acute myocardial infarction patients.BMC medical informatics and decision making · 2025Article
- Enhancer regulation in cancer: from epigenetics to mArchives of pharmacal research · 2025Review
- Generative artificial intelligence in cardiovascular specialty care: a scoping review.BMC nursing · 2025Article
- Multimodal AI-approach for the automatic screening of cardiovascular diseases based on nocturnal physiological signals.NPJ cardiovascular health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.
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What OpenQuestion holds
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.