{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T02:18:57Z","timestamp":1786501137975,"version":"build-2736575974"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"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. Microbiol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Severe Fever with Thrombocytopenia Syndrome (SFTS) is a disease caused by infection with the Severe Fever with Thrombocytopenia Syndrome virus (SFTSV), a novel Bunyavirus. Accurate prognostic assessment is crucial for developing individualized prevention and treatment strategies. However, machine learning prognostic models for SFTS are rare and need further improvement and clinical validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aims to develop and validate an interpretable prognostic model based on machine learning (ML) methods to enhance the understanding of SFTS progression.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This multicenter retrospective study analyzed patient data from two provinces in China. The derivation cohort included 292 patients treated at The Second Hospital of Nanjing from January 2022 to December 2023, with a 7:3 split for model training and internal validation. The external validation cohort consisted of 104 patients from The First Affiliated Hospital of Wannan Medical College during the same period. Twenty-four commonly available clinical features were selected, and the Boruta algorithm identified 12 candidate predictors, ranked by Z-scores, which were progressively incorporated into 10 machine learning models to develop prognostic models. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC), accuracy, recall, and F1 score. The clinical utility of the best-performing model was evaluated through decision curve analysis (DCA) based on net benefit. Robustness was tested with 10-fold cross-validation, and feature importance was explained using SHapley Additive exPlanation (SHAP) both globally and locally.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among the 10 machine learning models, the XGBoost model demonstrated the best overall discriminatory ability. Considering both AUC index and feature simplicity, a final interpretable XGBoost model with 7 key features was constructed. The model showed high predictive accuracy for patient outcomes in both internal (AUC = 0.911, 95% CI: 0.842\u20130.967) and external validations (AUC = 0.891, 95% CI: 0.786\u20130.977). A clinical tool based on this model has been developed and implemented using the Streamlit framework.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The interpretable XGBoost-based prognostic model for SFTS shows high predictive accuracy and has been translated into a clinical tool. The model's 7 key features serve as valuable indicators for early prognosis of SFTS, warranting close attention from healthcare professionals in clinical practice.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmicb.2025.1557922","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T07:11:58Z","timestamp":1742368318000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["A multicenter study on developing a prognostic model for severe fever with thrombocytopenia syndrome using machine learning"],"prefix":"10.3389","volume":"16","author":[{"given":"Jian-She","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Quan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Shan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/j.tig.2020.03.005","article-title":"Opening the black box: interpretable machine learning for geneticists","volume":"36","author":"Azodi","year":"2020","journal-title":"Trends Genet."},{"key":"B2","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.jclinepi.2022.01.011","article-title":"Population median imputation was noninferior to complex approaches for imputing missing values in cardiovascular prediction models in clinical practice","volume":"145","author":"Berkelmans","year":"2022","journal-title":"J. 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