ArticleClinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Machine Learning-Based Prediction of Pulmonary Embolism Prognosis Using Nutritional and Inflammatory Indices.
Article in Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Harnessing hybrid stacking ensemble learning for accurate pulmonary embolism diagnosis using tabular clinical data.Scientific reports · 2026Article
- An Integrated Clinical and Biomarker Model Using Penalized Regression to Predict In-Hospital Mortality in Acute Pulmonary Embolism.Journal of clinical medicine · 2026Article
- Clinical features and risk factors for poor prognosis in malignant tumor patients with pulmonary embolism: a case control study.Thrombosis journal · 2025Article
- Research progress of artificial intelligence and machine learning in pulmonary embolism.Frontiers in medicine · 2025Review
- Immunoinflammatory markers SIRI and NAR as predictors of respiratory distress syndrome and secondary infections in premature infants.Frontiers in cellular and infection microbiology · 2024Article
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Authors and funding
3 authors.
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Abstract
purposeThis study aimed to create and assess machine learning (ML) models that utilize nutritional and inflammatory indices, focusing on the advanced lung cancer inflammation index (ALI) and neutrophil-to-albumin ratio (NAR), to improve the prediction accuracy of PE prognosis. PATIENTS AND
methodsWe conducted a retrospective analysis of 312 patients, comprising 254 survivors and 58 non-survivors. The Boruta algorithm was used to identify significant variables, and four ML models (XGBoost, Random Forest, Logistic Regression, and SVM) were employed to analyze the clinical data and assess the performance of the models.
resultsThe XGBoost model, with optimal hyperparameters, achieved the best performance, with an accuracy of 0.882, an F1-score of 0.889, a precision of 0.917, a sensitivity of 0.863, a specificity of 0.905, and an AUC of 0.873. Analysis of feature importance indicated that the most critical predictors across models were respiratory failure, log-transformed ALI, albumin level, age, diastolic blood pressure, and NAR.
conclusionThe ML-based prediction models effectively predicted the prognosis of PE, with the XGBoost model exhibiting good performance. Respiratory failure, ALI, albumin level, age, diastolic blood pressure, and NAR were correlated with PE prognosis.
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