{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T05:52:30Z","timestamp":1785909150785,"version":"3.56.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pulmonary Embolism (PE) is a condition that results in significant mortality and morbidity, particularly among the elderly. The aim of our study is to utilize machine learning (ML) algorithms specifically tailored for this population, thereby enhancing the accuracy of risk assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Elderly patients with PE who were included between January 1, 2013, and December 31, 2023, were divided into two groups: training and validation sets. A total of five ML models, including decision tree, random forest (RF), extreme gradient boosting, support vector machine, and k-nearest neighbors, were developed to predict in-hospital mortality in these patients. The model demonstrating the best diagnostic performance was selected. Ultimately, the ML models were internally validated to assess their diagnostic performance using receiver operating characteristic analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The analysis included 250 patients, with 174 assigned to the training set and 76 to the validation set. The ML models were developed using nine clinical features: age, lactate levels, blood urea nitrogen, arterial oxygen partial pressure, red blood cell count, diastolic blood pressure, vasopressor use, acute kidney injury, and the requirement for continuous renal replacement therapy. Of the five evaluated ML models, the RF model demonstrated the best performance, attaining the highest area under the curve values of 0.950 for the training set and 0.835 for the validation set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML-based models exhibit strong predictive capabilities for identifying elderly patients with PE who are at risk of hospital mortality. These algorithms can aid physicians in the early detection of high-risk patients, facilitating timely and appropriate preventive interventions. Future prospective studies comparing ML models with established PE-specific risk stratification tools are warranted.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1879237","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T05:40:08Z","timestamp":1785908408000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive modeling for mortality risk in elderly patients with pulmonary embolism using machine learning algorithms"],"prefix":"10.3389","volume":"13","author":[{"given":"Tao","family":"Chu","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, Northern Jiangsu People\u2019s Hospital Affiliated to Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Northern Jiangsu People\u2019s Hospital Affiliated to Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingyu","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Northern Jiangsu People\u2019s Hospital Affiliated to Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Northern Jiangsu People\u2019s Hospital Affiliated to Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingcheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Northern Jiangsu People\u2019s Hospital Affiliated to Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Northern Jiangsu People\u2019s Hospital Affiliated to Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1186","DOI":"10.3390\/medicina58091186","article-title":"Intermediate-risk pulmonary embolism: a review of contemporary diagnosis, risk stratification and management.","volume":"58","author":"Machanahalli Balakrishna","year":"2022","journal-title":"Medicina"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1087702","DOI":"10.3389\/fcvm.2023.1087702","article-title":"Prediction of in-hospital adverse clinical outcomes in patients with pulmonary thromboembolism, machine learning based models.","volume":"10","author":"Jenab","year":"2023","journal-title":"Front Cardiovasc Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"10.1002\/rth2.12770","DOI":"10.1002\/rth2.12770","article-title":"The venous thrombosis registry in \u00d8stfold hospital (TROLL registry) - design and cohort description.","volume":"6","author":"J\u00f8rgensen","year":"2022","journal-title":"Res Pract Thromb Haemost"},{"key":"B4","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12880-022-00916-0","article-title":"A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography.","volume":"22","author":"Ajmera","year":"2022","journal-title":"BMC Med Imaging"},{"key":"B5","doi-asserted-by":"publisher","first-page":"21501327211054996","DOI":"10.1177\/21501327211054996","article-title":"Age-adjusted d-dimer in the prediction of pulmonary embolism: systematic review and meta-analysis.","volume":"12","author":"Iwuji","year":"2021","journal-title":"J Prim Care Community Health"},{"key":"B6","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1016\/j.chest.2019.05.021","article-title":"Age-stratified national trends in pulmonary embolism admissions.","volume":"156","author":"Pauley","year":"2019","journal-title":"Chest"},{"key":"B7","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/s00380-021-01924-w","article-title":"PESI score for predicting clinical outcomes in PE patients with right ventricular involvement.","volume":"37","author":"Natanzon","year":"2022","journal-title":"Heart Vessels"},{"key":"B8","doi-asserted-by":"publisher","first-page":"2270","DOI":"10.1111\/j.1538-7836.2012.04929.x","article-title":"Prospective comparison of clinical prognostic scores in elder patients with a pulmonary embolism.","volume":"10","author":"Zwierzina","year":"2012","journal-title":"J Thromb Haemost"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1016\/S0140-6736(20)30226-9","article-title":"Artificial intelligence and the future of global health.","volume":"395","author":"Schwalbe","year":"2020","journal-title":"Lancet"},{"key":"B10","doi-asserted-by":"publisher","first-page":"107696","DOI":"10.1016\/j.compbiomed.2023.107696","article-title":"Advancing prognostic precision in pulmonary embolism: a clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification.","volume":"167","author":"Sadegh-Zadeh","year":"2023","journal-title":"Comput Biol Med"},{"key":"B11","doi-asserted-by":"publisher","first-page":"100165","DOI":"10.1016\/j.ibmed.2024.100165","article-title":"Outcome prediction for adult mechanically ventilated patients using machine learning models and comparison with conventional statistical methods: a single-centre retrospective study.","volume":"10","author":"Ong","year":"2024","journal-title":"Intell Based Med."},{"key":"B12","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s40001-023-01593-7","article-title":"Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.","volume":"29","author":"Zhou","year":"2024","journal-title":"Eur J Med Res"},{"key":"B13","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1186\/s12872-023-03363-z","article-title":"Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism.","volume":"23","author":"Wang","year":"2023","journal-title":"BMC Cardiovasc Disord"},{"key":"B14","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1093\/eurheartj\/ehz405","article-title":"2019 ESC guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European respiratory society (ERS).","volume":"41","author":"Konstantinides","year":"2020","journal-title":"Eur Heart J"},{"key":"B15","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1093\/eurheartj\/ehr392","article-title":"Predictors of in-hospital mortality in elderly patients with acute venous thrombo-embolism: the SWIss venous thromboembolism registry (SWIVTER).","volume":"33","author":"Spirk","year":"2012","journal-title":"Eur Heart J"},{"key":"B16","doi-asserted-by":"publisher","first-page":"JC4","DOI":"10.7326\/0003-4819-156-8-201204170-02012","article-title":"ACP journal club. The simplified pulmonary embolism severity index and high-sensitivity troponin t predicted prognosis in PE.","volume":"156","author":"Dunn","year":"2012","journal-title":"Ann Intern Med"},{"key":"B17","doi-asserted-by":"publisher","first-page":"3016","DOI":"10.1016\/j.jtha.2023.05.003","article-title":"Risk stratification of acute pulmonary embolism.","volume":"21","author":"de Wit","year":"2023","journal-title":"J Thromb Haemost"},{"key":"B18","doi-asserted-by":"publisher","first-page":"e12983","DOI":"10.1002\/emp2.12983","article-title":"Comparing predictive performance of pulmonary embolism risk stratification tools for acute clinical deterioration.","volume":"4","author":"Weekes","year":"2023","journal-title":"J Am Coll Emerg Physicians Open"},{"key":"B19","doi-asserted-by":"publisher","first-page":"e70206","DOI":"10.1111\/nicc.70206","article-title":"Machine learning for predicting mortality in intensive care unit patients: a prognostic performance systematic review and meta-analysis.","volume":"30","author":"Sun","year":"2025","journal-title":"Nurs Crit Care"},{"key":"B20","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1007\/s11739-025-03896-5","article-title":"Enhancing mortality prediction in intensive care units: improving APACHE II, SOFA, and SAPS II scoring systems using long short-term memory.","volume":"20","author":"Deng","year":"2025","journal-title":"Intern Emerg Med"},{"key":"B21","doi-asserted-by":"publisher","first-page":"108589","DOI":"10.1016\/j.cmpb.2025.108589","article-title":"Machine learning-based 28-day mortality prediction model for elderly neurocritically Ill patients.","volume":"260","author":"Yuan","year":"2025","journal-title":"Comput Methods Programs Biomed"},{"key":"B22","doi-asserted-by":"publisher","first-page":"103763","DOI":"10.1016\/j.jbi.2021.103763","article-title":"A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.","volume":"117","author":"Speiser","year":"2021","journal-title":"J Biomed Inform"},{"key":"B23","doi-asserted-by":"publisher","first-page":"5245","DOI":"10.1038\/s41598-020-62133-5","article-title":"Study of cardiovascular disease prediction model based on random forest in eastern China.","volume":"10","author":"Yang","year":"2020","journal-title":"Sci Rep"},{"key":"B24","first-page":"277","article-title":"Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records.","volume":"8","author":"Emir","year":"2015","journal-title":"J Pain Res"},{"key":"B25","doi-asserted-by":"publisher","first-page":"CD010222","DOI":"10.1002\/14651858.CD010222.pub4","article-title":"Anticoagulant treatment for subsegmental pulmonary embolism.","volume":"2","author":"Yoo","year":"2020","journal-title":"Cochrane Database Syst Rev"},{"key":"B26","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.thromres.2020.07.026","article-title":"Comparison of the wells score and the revised Geneva score as a tool to predict pulmonary embolism in outpatients over age 65.","volume":"196","author":"Coelho","year":"2020","journal-title":"Thromb Res"},{"key":"B27","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1111\/imj.14824","article-title":"Pulmonary embolism: clinical presentation and diagnosis in the oldest old.","volume":"50","author":"Chan","year":"2020","journal-title":"Intern Med J"},{"key":"B28","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1186\/s12876-024-03308-6","article-title":"Construction and validation of a nomogram for predicting survival in elderly patients with severe acute pancreatitis: a retrospective study from a tertiary center.","volume":"24","author":"Zhu","year":"2024","journal-title":"BMC Gastroenterol"},{"key":"B29","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.ejim.2021.01.021","article-title":"Venous lactate improves the prediction of in-hospital adverse outcomes in normotensive pulmonary embolism.","volume":"86","author":"Ebner","year":"2021","journal-title":"Eur J Intern Med"},{"key":"B30","doi-asserted-by":"publisher","first-page":"e232","DOI":"10.1161\/CIR.0000000000000525","article-title":"Contemporary management of cardiogenic shock: a scientific statement from the American heart association.","volume":"136","author":"van Diepen","year":"2017","journal-title":"Circulation"},{"key":"B31","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1007\/s11239-019-01906-w","article-title":"A narrative review of red blood cell distribution width as a marker for pulmonary embolism.","volume":"48","author":"Hammons","year":"2019","journal-title":"J Thromb Thrombolysis"},{"key":"B32","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1111\/j.1538-7836.2010.04164.x","article-title":"A local increase in red blood cell aggregation can trigger deep vein thrombosis: evidence based on quantitative cellular ultrasound imaging.","volume":"9","author":"Yu","year":"2011","journal-title":"J Thromb Haemost"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1879237\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T05:40:09Z","timestamp":1785908409000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1879237\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,5]]},"references-count":32,"alternative-id":["10.3389\/fmed.2026.1879237"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1879237","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,5]]},"article-number":"1879237"}}