ReviewJournal of geriatric cardiology : JGC2019
Current applications of big data and machine learning in cardiology.
Review in Journal of geriatric cardiology : JGC, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04682756 (A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model), which is not on this map. Cited by 41 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.
A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model
Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis.Neuroradiology · 2021Pooled it
- Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review.Medicina (Kaunas, Lithuania) · 2021Pooled it
- Article
- Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review.Journal of clinical medicine · 2025Review
- RETRACTED: Artificial intelligence for emergency medical care.Health care science · 2025Review
- Novel strategies to FIND people living with genetic dyslipidemias: The family heart foundation flag, identify, network, and deliver (FIND) familial hypercholesterolemia collaborative learning network.American journal of preventive cardiology · 2025Review
- Predicting high-risk factors for postoperative inadequate analgesia and adverse reactions in cesarean delivery surgery: a prospective study.International journal of surgery (London, England) · 2025Article
- Machine learning for risk prediction of acute kidney injury in patients with diabetes mellitus combined with heart failure during hospitalization.Scientific reports · 2025Article
- The Role of Artificial Intelligence in Cardiovascular Disease Risk Prediction: An Updated Review on Current Understanding and Future Research.Current cardiology reviews · 2025Review
- Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?Current atherosclerosis reports · 2024Review
- Prediction of post-delivery hemoglobin levels with machine learning algorithms.Scientific reports · 2024Article
- Review
- Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol.BMJ open · 2024Article
- Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms.Biophysics reviews · 2024Review
- Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.Frontiers in computational neuroscience · 2024Article
- Applications of artificial intelligence-powered prenatal diagnosis for congenital heart disease.Frontiers in cardiovascular medicine · 2024Review
- Article
- Advancements in artificial intelligence-driven techniques for interventional cardiology.Cardiology journal · 2024Review
- Characteristics and Admission Preferences of Pediatric Emergency Patients and Their Waiting Time Prediction Using Electronic Medical Record Data: Retrospective Comparative Analysis.Journal of medical Internet research · 2023Article
- Including myocardial flow reserve by PET in prediction models: Ready to fly?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
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
Machine learning (ML) is a software solution with the ability of making predictions without prior explicit programming, aiding in the analysis of large amounts of data. These algorithms can be trained through supervised or unsupervised learning. Cardiology is one of the fields of medicine with the highest interest in its applications. They can facilitate every step of patient care, reducing the margin of error and contributing to precision medicine. In particular, ML has been proposed for cardiac imaging applications such as automated computation of scores, differentiation of prognostic phenotypes, quantification of heart function and segmentation of the heart. These tools have also demonstrated the capability of performing early and accurate detection of anomalies in electrocardiographic exams. ML algorithms can also contribute to cardiovascular risk assessment in different settings and perform predictions of cardiovascular events. Another interesting research avenue in this field is represented by genomic assessment of cardiovascular diseases. Therefore, ML could aid in making earlier diagnosis of disease, develop patient-tailored therapies and identify predictive characteristics in different pathologic conditions, leading to precision cardiology.
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.