ReviewCell reports. Medicine2022
Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective.
Review in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 16 citations in OpenAlex.
- An interpretable machine learning model based on routine clinical indicators for early identification of obesity-related non-alcoholic fatty liver disease in children: a single-center retrospective study.Translational pediatrics · 2026Article
- Machine learning in cardiology education: preparing the next generation for the AI era.The British journal of cardiology · 2026Article
- Machine learning models for predicting renal injury in patients with gout.Renal failure · 2025Article
- Applied machine learning to predict 1-year major adverse cardiovascular events in elderly patients after percutaneous coronary intervention.BMC medical informatics and decision making · 2025Article
- Machine learning-based integration of pericoronary adipose tissue and clinical risk factors for cardiovascular risk prediction in type 2 diabetes: a retrospective cohort study.European journal of medical research · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.Cell reports. Medicine · 2024Article
- [Research progress on bioinformatics in pulmonary arterial hypertension].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2024Review
- A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population.Frontiers in public health · 2024Article
- Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve.Diagnostics (Basel, Switzerland) · 2023Article
- WGCNA combined with machine learning algorithms for analyzing key genes and immune cell infiltration in heart failure due to ischemic cardiomyopathy.Frontiers in cardiovascular medicine · 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
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in machine learning (ML) have made it possible to analyze high-dimensional and complex data-such as free text, images, waveforms, videos, and sound-in an automated manner by successfully learning complex associations within these data. Cardiovascular medicine is particularly well poised to take advantage of these ML advances, due to the widespread digitization of medical data and the large number of diagnostic tests used to evaluate cardiovascular disease. Various ML approaches have successfully been applied to cardiovascular tests and diseases to automate interpretation, accurately perform measurements, and, in some cases, predict novel diagnoses from less invasive tests, effectively expanding the utility of more widely accessible diagnostic tests. Here, we present examples of some impactful advances in cardiovascular medicine using ML across a variety of modalities, with a focus on deep learning applications.
Indexed as
Identifiers
What Socratic 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 Socratic graph.