ReviewCurrent atherosclerosis reports2023
Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches.
Review in Current atherosclerosis reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07539532 (Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery), which is not on this map. Cited by 18 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.
Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Research on machine learning-based clinical prediction models: a bibliometric analysis.Frontiers in oncology · 2026Pooled it
- Opportunities and Challenges of Cardiovascular Disease Risk Prediction for Primary Prevention Using Machine Learning and Electronic Health Records: A Systematic Review.Reviews in cardiovascular medicine · 2025Pooled it
- Article
- Explainable machine-learning model for coronary artery disease diagnosis in non-AMI patients: integrating inflammatory and traditional risk factors.Open heart · 2026Article
- Biological Cardiovascular Age Derived from Coronary CTA Reports Using a Large Language Model: A Novel Predictor of Major Adverse Cardiovascular Events?Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Consensus machine learning identifies cell death gene signature for carotid artery stenosis diagnosis.iScience · 2026Article
- Development of an explainable machine learning model for 3-year cardiovascular risk prediction in new-onset type 2 diabetes using the TyG index and ultrasound features.BMC medical informatics and decision making · 2025Article
- Interpretable machine learning model for cardiovascular disease risk prediction: a feature decomposition-based study.BMC public health · 2025Article
- Association of DNA methylation epigenetic markers with all-cause mortality and cardiovascular disease-related mortality in diabetic population: a machine learning-based retrospective cohort study.Diabetology & metabolic syndrome · 2025Article
- Machine Learning-Based Immuno-Inflammatory Index Integrating Clinical Characteristics for Predicting Coronary Artery Plaque Rupture.Immunity, inflammation and disease · 2025Article
- Explainable machine learning model of coronary artery disease combined with diabetes: development and validation study.Frontiers in cardiovascular medicine · 2025Article
- A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions.Frontiers in artificial intelligence · 2025Review
- Development and Validation of Deep Learning Model for Predicting Long-Term Prognosis in Patients with Symptomatic Intracranial Arterial Stenosis.International journal of general medicine · 2025Article
- Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records.BMC medical research methodology · 2024Article
- Machine learning-driven risk assessment of coronary heart disease: Analysis of NHANES data from 1999 to 2018.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Article
- Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.Biomedical engineering online · 2024Article
- Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning.BMC medical informatics and decision making · 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
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewIn this review, we sought to provide an overview of ML and focus on the contemporary applications of ML in cardiovascular risk prediction and precision preventive approaches. We end the review by highlighting the limitations of ML while projecting on the potential of ML in assimilating these multifaceted aspects of CAD in order to improve patient-level outcomes and further population health. RECENT
findingsCoronary artery disease (CAD) is estimated to affect 20.5 million adults across the USA, while also impacting a significant burden at the socio-economic level. While the knowledge of the mechanistic pathways that govern the onset and progression of clinical CAD has improved over the past decade, contemporary patient-level risk models lag in accuracy and utility. Recently, there has been renewed interest in combining advanced analytic techniques that utilize artificial intelligence (AI) with a big data approach in order to improve risk prediction within the realm of CAD. By virtue of being able to combine diverse amounts of multidimensional horizontal data, machine learning has been employed to build models for improved risk prediction and personalized patient care approaches. The use of ML-based algorithms has been used to leverage individualized patient-specific data and the associated metabolic/genomic profile to improve CAD risk assessment. While the tool can be visualized to shift the paradigm toward a patient-specific care, it is crucial to acknowledge and address several challenges inherent to ML and its integration into healthcare before it can be significantly incorporated in the daily clinical practice.
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.