ReviewPulmonary circulation2025
Advancing Mortality Prediction in Pulmonary Embolism Using Machine Learning Algorithms-Systematic Review and Meta-Analysis.
Review in Pulmonary circulation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- "Diagnostic Performance of Artificial Intelligence in Evaluating Tricuspid Regurgitation: A Systematic Review and Meta-Analysis".Clinical cardiology · 2026Pooled it
- Optimizing Intubation Prediction in Pneumonia Patients: A Systematic Review and Meta-Analysis of Machine Learning Algorithms.Pulmonary medicine · 2026Pooled it
- Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department.BMC emergency medicine · 2026Article
- Accuracy of machine learning models for mitral regurgitation severity assessment: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Accuracy of machine learning models in predicting recurrent venous thromboembolism: a systematic review and meta-analysis.Journal of thrombosis and thrombolysis · 2026Review
- Machine learning and Regression-Based models for prediction of postoperative atrial fibrillation following coronary artery bypass grafting: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Harnessing hybrid stacking ensemble learning for accurate pulmonary embolism diagnosis using tabular clinical data.Scientific reports · 2026Article
- Efficacy and comparative performance of machine learning models for stroke risk prediction in hypertensive patients: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Predicting Mortality in Pulmonary Embolism: A Machine Learning Approach with External Validation in COVID-19 Patients.Medicina (Kaunas, Lithuania) · 2026Article
- An Integrated Clinical and Biomarker Model Using Penalized Regression to Predict In-Hospital Mortality in Acute Pulmonary Embolism.Journal of clinical medicine · 2026Article
- Artificial Intelligence-Driven Integration of ECG and Molecular Biomarkers in Pulmonary Embolism.International journal of molecular sciences · 2026Review
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
This systematic review and meta-analysis evaluated the performance of machine learning (ML) models in predicting mortality among pulmonary embolism (PE) patients, synthesizing data from 17 studies encompassing 844,071 cases. Logistic Regression was the most commonly used algorithm, followed by advanced models like Random Forests, Support Vector Machines, XGBoost, and Neural Networks. Pooled performance metrics from 12 studies demonstrated a sensitivity of 0.88 (95% CI: 0.78-0.94,
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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.