ArticlePulmonary circulation2022
Predicting pulmonary embolism among hospitalized patients with machine learning algorithms.
Article in Pulmonary circulation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in the Prediction of Venous Thromboembolism: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Characterization of pulmonary embolism events in patients with suspected pneumonia.Internal and emergency medicine · 2026Article
- Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department.BMC emergency medicine · 2026Article
- Depressive and anxiety symptoms in COPD: associations with diffusion impairment and echocardiographic pulmonary hemodynamic burden.BioPsychoSocial medicine · 2026Article
- Risk stratification in pulmonary embolism: current evidence and future directions.The Journal of cardiovascular surgery · 2026Review
- Emergency Department Prediction of In-Hospital Mortality in Suspected Pulmonary Embolism: An Explainable Machine Learning Approach.Journal of clinical medicine · 2026Article
- Machine learning improves prediction of pulmonary thromboembolism and reduces unnecessary computed tomography scans in the emergency department.Scientific reports · 2026Article
- Advancements in Pulmonary Embolism Management: Current Approaches and Future Directions.Reviews in cardiovascular medicine · 2025Review
- Performance of Microsoft Copilot in the Diagnostic Process of Pulmonary Embolism.The western journal of emergency medicine · 2025Article
- Performance of Plug-In Augmented ChatGPT and Its Ability to Quantify Uncertainty: Simulation Study on the German Medical Board Examination.JMIR medical education · 2025Article
- Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism.Frontiers in medicine · 2025Article
- Development of a predictive nomogram for early identification of pulmonary embolism in hospitalized patients: a retrospective cohort study.BMC pulmonary medicine · 2024Article
- Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study.Journal of medical Internet research · 2024Article
- Availability in ECMO Reduces the Failure to Rescue in Patients With Pulmonary Embolism After Major Surgery: A Nationwide Analysis of 2.4 Million Cases.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024Article
- Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Role of artificial intelligence in oncologic emergencies: a narrative review.Exploration of targeted anti-tumor therapy · 2023Review
- Predicting pulmonary embolism among hospitalized patients with machine learning algorithms.Pulmonary circulation · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Pulmonary embolisms (PE) are life-threatening medical events, and early identification of patients experiencing a PE is essential to optimizing patient outcomes. Current tools for risk stratification of PE patients are limited and unable to predict PE events before their occurrence. Objective: We developed a machine learning algorithm (MLA) designed to identify patients at risk of PE before the clinical detection of onset in an inpatient population. Materials and Methods: Three machine learning (ML) models were developed on electronic health record data from 63,798 medical and surgical inpatients in a large US medical center. These models included logistic regression, neural network, and gradient boosted tree (XGBoost) models. All models used only routinely collected demographic, clinical, and laboratory information as inputs. All were evaluated for their ability to predict PE at the first time patient vital signs and lab measures required for the MLA to run were available. Performance was assessed with regard to the area under the receiver operating characteristic (AUROC), sensitivity, and specificity. Results: The model trained using XGBoost demonstrated the strongest performance for predicting PEs. The XGBoost model obtained an AUROC of 0.85, a sensitivity of 81%, and a specificity of 70%. The neural network and logistic regression models obtained AUROCs of 0.74 and 0.67, sensitivity of 81% and 81%, and specificity of 44% and 35%, respectively. Conclusions: This algorithm may improve patient outcomes through earlier recognition and prediction of PE, enabling earlier diagnosis and treatment of PE.
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