ArticleJournal of cardiovascular development and disease2022
Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction.
Article in Journal of cardiovascular development and disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning-based myocardial infarction bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review).Sovremennye tekhnologii v meditsine · 2024Pooled 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-based approaches for monitoring medication adherence among cardiovascular disease patients: a scoping review.BMC medical informatics and decision making · 2026Article
- Development and validation of an in-hospital cardiogenic shock prediction model for AMI patients based on machine learning.BMC cardiovascular disorders · 2026Article
- An Innovative Model for Diagnosing Lesions in Coronary Angiography Imagery Using an Improved YOLOv4 Model.Bioengineering (Basel, Switzerland) · 2025Article
- Comparison of the Prognostic Performance of Various Machine Learning Models in Patients with Acute Myocardial Infarction: Results from the COREA-AMI Registry.Medicina (Kaunas, Lithuania) · 2025Article
- Machine learning integration of echocardiographic and clinical data to improve prediction of survival following myocardial infarction.European heart journal open · 2025Article
- Advances and applications of biosensors in pulmonary hypertension.Respiratory research · 2025Review
- Early prediction of microvascular obstruction prior to percutaneous coronary intervention.Scientific reports · 2025Article
- Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.European heart journal. Digital health · 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 for predicting acute myocardial infarction in patients with sepsis.Scientific reports · 2024Article
- Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms.Scientific reports · 2024Article
- Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.PloS one · 2024Article
- Top-Down Proteomics of Human Saliva, Analyzed with Logistic Regression and Machine Learning Methods, Reveal Molecular Signatures of Ovarian Cancer.International journal of molecular sciences · 2023Article
- A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases.Healthcare (Basel, Switzerland) · 2023Review
- Recent Advances in the Roles of MicroRNA and MicroRNA-Based Diagnosis in Neurodegenerative Diseases.Biosensors · 2022Review
- A simple APACHE IV risk dynamic nomogram that incorporates early admitted lactate for the initial assessment of 28-day mortality in critically ill patients with acute myocardial infarction.BMC cardiovascular disorders · 2022Article
- A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection.Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
(1) Background: Patients with acute myocardial infarction (AMI) still experience many major adverse cardiovascular events (MACEs), including myocardial infarction, heart failure, kidney failure, coronary events, cerebrovascular events, and death. This retrospective study aims to assess the prognostic value of machine learning (ML) for the prediction of MACEs. (2) Methods: Five-hundred patients diagnosed with AMI and who had undergone successful percutaneous coronary intervention were included in the study. Logistic regression (LR) analysis was used to assess the relevance of MACEs and 24 selected clinical variables. Six ML models were developed with five-fold cross-validation in the training dataset and their ability to predict MACEs was compared to LR with the testing dataset. (3) Results: The MACE rate was calculated as 30.6% after a mean follow-up of 1.42 years. Killip classification (Killip IV vs. I class, odds ratio 4.386, 95% confidence interval 1.943-9.904), drug compliance (irregular vs. regular compliance, 3.06, 1.721-5.438), age (per year, 1.025, 1.006-1.044), and creatinine (1 µmol/L, 1.007, 1.002-1.012) and cholesterol levels (1 mmol/L, 0.708, 0.556-0.903) were independent predictors of MACEs. In the training dataset, the best performing model was the random forest (RDF) model with an area under the curve of (0.749, 0.644-0.853) and accuracy of (0.734, 0.647-0.820). In the testing dataset, the RDF showed the most significant survival difference (log-rank
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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.