ArticleFrontiers in cardiovascular medicine2021
A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 38 citations in OpenAlex.
- Explainable machine learning model reveals its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation.BMC cardiovascular disorders · 2023Trial
- Application of Artificial Intelligence to an Impact-Based Analysis Method to Estimate the Soft Tissue Phantom Stiffness: A Proof-of-Concept.Sensors (Basel, Switzerland) · 2026Article
- Deep learning for cardiovascular disease: a comprehensive review of detection and risk forecasting.Frontiers in artificial intelligence · 2026Review
- Machine learning predictive system to predict the risk of developing pre-eclampsia.BMJ health & care informatics · 2025Article
- Review
- Exploration of Risk Factors for Cardiovascular Disease in Patients with Rheumatoid Arthritis: A Retrospective Study.Inflammation · 2025Article
- Integrating deep learning features from mammography with SHAP values for a machine learning model predicting over 5-year recurrence of breast ductal carcinomaFrontiers in immunology · 2025Article
- Preeclampsia prediction via machine learning: a systematic literature review.Health systems (Basingstoke, England) · 2025Review
- PARCCS: A Machine Learning Risk-Prediction Model for Acute Peripartum Cardiovascular Complications During Delivery Admissions.JACC. Advances · 2024Article
- Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension.Clinics and practice · 2024Review
- Machine learning-based models for the prediction of breast cancer recurrence risk.BMC medical informatics and decision making · 2023Article
- Potential Roles of microRNAs for Assessing Cardiovascular Risk in Pre-Eclampsia-Exposed Postpartum Women and Offspring.International journal of molecular sciences · 2023Review
- Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture.Bioengineering (Basel, Switzerland) · 2022Article
- Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.Frontiers in cardiovascular medicine · 2022Article
- Deep learning methods may not outperform other machine learning methods on analyzing genomic studies.Frontiers in genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.