ArticleBMC cardiovascular disorders2023
Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism.
Article in BMC cardiovascular disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Harnessing hybrid stacking ensemble learning for accurate pulmonary embolism diagnosis using tabular clinical data.Scientific reports · 2026Article
- Risk stratification in pulmonary embolism: current evidence and future directions.The Journal of cardiovascular surgery · 2026Review
- Relationship Between the Lactate-to-Albumin Ratio and Acute Kidney Injury in Patients with Pulmonary Embolism: A Retrospective Cohort Study.Medicina (Kaunas, Lithuania) · 2026Article
- Emergency Department Prediction of In-Hospital Mortality in Suspected Pulmonary Embolism: An Explainable Machine Learning Approach.Journal of clinical medicine · 2026Article
- An Integrated Clinical and Biomarker Model Using Penalized Regression to Predict In-Hospital Mortality in Acute Pulmonary Embolism.Journal of clinical medicine · 2026Article
- Predictive modeling for mortality risk in elderly patients with pulmonary embolism using machine learning algorithms.Frontiers in medicine · 2026Article
- Letter to the Editor, 'Haemodynamic consequences of acute pulmonary embolism predict risk of CTPA-related acute kidney injury'.ESC heart failure · 2025Article
- Development and validation of a nomogram for predicting the occurrence of acute kidney injury in patients with pulmonary embolism.Renal failure · 2025Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Performance of Microsoft Copilot in the Diagnostic Process of Pulmonary Embolism.The western journal of emergency medicine · 2025Article
- Advancing Mortality Prediction in Pulmonary Embolism Using Machine Learning Algorithms-Systematic Review and Meta-Analysis.Pulmonary circulation · 2025Review
- Prediction of pulmonary embolism by an explainable machine learning approach in the real world.Scientific reports · 2025Article
- Research progress of artificial intelligence and machine learning in pulmonary embolism.Frontiers in medicine · 2025Review
- Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery.Journal of medical systems · 2024Review
- Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods.Frontiers in cardiovascular medicine · 2024Article
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe aimed to use machine learning (ML) algorithms to risk stratify the prognosis of critical pulmonary embolism (PE). MATERIAL AND
methodsIn total, 1229 patients were obtained from MIMIC-IV database. Main outcomes were set as all-cause mortality within 30 days. Logistic regression (LR) and simplified eXtreme gradient boosting (XGBoost) were applied for model constructions. We chose the final models based on their matching degree with data. To simplify the model and increase its usefulness, finally simplified models were built based on the most important 8 variables. Discrimination and calibration were exploited to evaluate the prediction ability. We stratified the risk groups based on risk estimate deciles.
resultsThe simplified XGB model performed better in model discrimination, which AUC were 0.82 (95% CI: 0.78-0.87) in the validation cohort, compared with the AUC of simplified LR model (0.75 [95% CI: 0.69-0.80]). And XGB performed better than sPESI in the validation cohort. A new risk-classification based on XGB could accurately predict low-risk of mortality, and had high consistency with acknowledged risk scores.
conclusionsML models can accurately predict the 30-day mortality of critical PE patients, which could further be used to reduce the burden of ICU stay, decrease the mortality and improve the quality of life for critical PE patients.
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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.