{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T07:30:30Z","timestamp":1780990230357,"version":"3.54.1"},"reference-count":17,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"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>Objective<\/jats:title>\n                    <jats:p>Sepsis-associated thrombocytopenia is a frequent complication of sepsis and is associated with poor clinical outcomes. Accurate diagnosis remains challenging, and machine learning approaches may offer improved diagnostic performance. The objective of this study was to conduct a comparative assessment of the performance of machine learning models (Random Forest, Artificial Neural Networks, Extreme Gradient Boosting, and Naive Bayes) in diagnosing sepsis-associated thrombocytopenia and to evaluate their diagnostic performance based on predefined performance criteria.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cross-sectional study was conducted at two centers and utilized data from 1,447 sepsis patients extracted from electronic health records between January 2013 and December 2023. The dataset comprised demographic and clinical attributes together with laboratory test results. The data were partitioned into training and test sets in an 80:20 ratio. All models were trained on the training set, and 10-fold cross-validation was applied within the training set to assess internal performance consistency. Model performance was evaluated using accuracy, precision, F1 score, AUROC, and confusion matrix. The ten most significant variables were ranked using classifier-based feature importance and SHAP analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Thrombocytopenia occurred in 772 (53.4%) of the 1,447 sepsis patients. Cross-validation results indicated that XGBoost exhibited superior performance, achieving an accuracy of 91.10% and an F1 score of 92.31%. ANN and RF achieved accuracies of 90.32% and 83.75%, with F1 scores of 91.51% and 85.32%, respectively. The AUROC values for cross-validation and test sets of RF, ANN, XGBoost, and Naive Bayes models exceeded 90%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Among the evaluated algorithms, the XGBoost model demonstrated superior performance, achieving an AUROC of 98.60% in cross-validation and 97.50% in the test set, indicating the strongest performance metrics within internal validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1715551","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T07:49:01Z","timestamp":1770623341000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["XGBoost outperforms other machine learning models in diagnosing Sepsis-Associated Thrombocytopenia: a multicenter retrospective study"],"prefix":"10.3389","volume":"13","author":[{"given":"Busra","family":"Emir","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Faculty of Medicine, Izmir Katip Celebi University","place":["Izmir, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evrim","family":"Ozmen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Institute of Health Sciences, Izmir Katip Celebi University","place":["Izmir, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sukriye Miray","family":"Kilincer Bozgul","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Faculty of Medicine, Ege University","place":["Izmir, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caner","family":"Acar","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Faculty of Medicine, Ege University","place":["Izmir, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nur","family":"Soyer","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Faculty of Medicine, Ege University","place":["Izmir, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devrim","family":"Bozkurt","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Faculty of Medicine, Ege University","place":["Izmir, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamil","family":"Gonderen","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Faculty of Medicine, Izmir Katip Celebi University","place":["Izmir, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet G\u00f6ktu\u011f","family":"Efgan","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Faculty of Medicine, Izmir Katip Celebi University","place":["Izmir, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.3390\/diagnostics13061060","article-title":"Applications of artificial intelligence in thrombocytopenia","volume":"13","author":"Elshoeibi","year":"2023","journal-title":"Diagnostics"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.medin.2018.07.016","article-title":"A machine learning-based model for 1-year mortality prediction in patients admitted to an intensive care unit with a diagnosis of sepsis","volume":"44","author":"Garc\u00eda-Gallo","year":"2020","journal-title":"Med Intensiva"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA American Med Association"},{"key":"ref4","volume-title":"Global report on the epidemiology and burden of sepsis: Current evidence, identifying gaps and future directions","year":"2020"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1007\/s00134-024-07478-5","article-title":"Ten tips on sepsis-induced thrombocytopenia","volume":"50","author":"Leone","year":"2024","journal-title":"Intensive Care Med"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"3494","DOI":"10.3390\/ijms20143494","article-title":"Platelets are critical key players in sepsis","volume":"20","author":"Vardon-Bounes","year":"2019","journal-title":"Int J Mol Sci MDPI AG"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.ijid.2020.06.045","article-title":"Recombinant human thrombopoietin in critically ill patients with sepsis-associated thrombocytopenia: a clinical 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A prospective study","volume":"13","author":"Luka","year":"2024","journal-title":"J Clin Med"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"3","DOI":"10.5152\/dcbybd.2015.808","article-title":"Yo\u011fun bak\u0131m \u00dcnitesinde yatan hastalarda trombositopeni s\u0131kl\u0131\u011f\u0131 ve trombositopeni geli\u015fimini etkileyen fakt\u00f6rler","volume":"7","author":"Co\u015fkun","year":"2016","journal-title":"J Med Surg Intensive Care Med"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"E221","DOI":"10.1097\/CCM.0000000000002919","article-title":"Platelet drop and fibrinolytic shutdown in patients with sepsis","volume":"46","author":"Semeraro","year":"2018","journal-title":"Crit Care Med"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1715551\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T07:49:02Z","timestamp":1770623342000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1715551\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":17,"alternative-id":["10.3389\/fmed.2026.1715551"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1715551","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]},"article-number":"1715551"}}