ReviewBasic research in cardiology2023
A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction.
Review in Basic research in cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning-based risk predictive models for depression in patients with diabetes: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Unraveling Atherosclerosis through Multi-omics: Systematic Insights into the Unique Applications and Clinical Perspectives.Current atherosclerosis reports · 2026Review
- Development and validation of an explainable machine learning model for differentiating diabetic nephropathy from diabetic retinopathy in patients with type 2 diabetes.Frontiers in endocrinology · 2026Article
- Interpretable machine learning model for cardiovascular disease risk prediction: a feature decomposition-based study.BMC public health · 2025Article
- Integrating Large-Scale Data Analytics for Cardiovascular Disease Prediction: A Scoping Review.Healthcare informatics research · 2025Article
- The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES.BMC public health · 2025Article
- Machine Learning Based Prediction of Postoperative Acute Kidney Injury Risk in Coronary Artery Bypass Grafting Patients.Clinical interventions in aging · 2025Article
- Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction.Frontiers in cardiovascular medicine · 2025Article
- Fluid status assessment in heart failure patients: pilot validation of the Maastricht Decompensation Questionnaire.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2025Article
- Predicting the risk of type 2 diabetes mellitus (T2DM) emergence in 5 years using mammography images: a comparison study between radiomics and deep learning algorithm.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Cardiovascular Risk Estimation in Colombia Using Artificial Intelligence Techniques.Cardiology research and practice · 2025Article
- The promise of machine learning models in cardiovascular risk prediction : Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction.Internal and emergency medicine · 2024Article
- AI hybrid survival assessment for advanced heart failure patients with renal dysfunction.Nature communications · 2024Article
- Central stress pathways in the development of cardiovascular disease.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2024Review
- Application of three-dimensional printing in the planning and execution of aortic aneurysm repair.Frontiers in cardiovascular medicine · 2024Review
- Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study.BMC medical informatics and decision making · 2023Article
- Article
- Personalized Management for Heart Failure with Preserved Ejection Fraction.Journal of personalized medicine · 2023Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.
Indexed as
Identifiers
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.