ArticleFrontiers in cardiovascular medicine2024
Prediction of longitudinal clinical outcomes after acute myocardial infarction using a dynamic machine learning algorithm.
Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Accuracy of Machine Learning Models for Early Prediction of Major Cardiovascular Events Post Myocardial Infarction: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2025Pooled it
- Machine learning to predict long-term cardiovascular death following myocardial infarction: incremental value of echocardiographic data.European heart journal. Digital health · 2026Article
- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
- Risk prediction models for cardiac rupture after acute myocardial infarction: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Review
- Machine Learning Approach on Predictive Model Establishment for In-Hospital Mortality in Acute Myocardial Infarction Patients Post-Percutaneous Coronary Intervention: Solutions for Databases With Dimensionality Reduction and Class Imbalance.Reviews in cardiovascular medicine · 2025Article
- Twenty-Year Clinical Experience of the Korea Acute Myocardial Infarction Registry.Korean circulation journal · 2025Review
- Machine learning integration of echocardiographic and clinical data to improve prediction of survival following myocardial infarction.European heart journal open · 2025Article
- Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes.BMC cardiovascular disorders · 2025Article
- Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification.Frontiers in artificial intelligence · 2025Article
- Physiological-immune resilience risk assessment model for predicting adverse cardiac outcomes in patients with acute myocardial infarction.Frontiers in cardiovascular medicine · 2025Article
- Machine learning models for predicting risks of MACEs for myocardial infarction patients with different VEGFR2 genotypes.Frontiers in medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several regression-based models for predicting outcomes after acute myocardial infarction (AMI) have been developed. However, prediction models that encompass diverse patient-related factors over time are limited. This study aimed to develop a machine learning-based model to predict longitudinal outcomes after AMI. This study was based on a nationwide prospective registry of AMI in Korea (
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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.