{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T08:26:25Z","timestamp":1769675185914,"version":"3.49.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>This study aimed to use machine learning to evaluate the risk factors of seizures and develop a model and nomogram to predict seizures in children with coronavirus disease 2019 (COVID-19).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Material and methods<\/jats:title>\n                  <jats:p>A total of 519 children with COVID-19 were assessed to develop predictive models using machine learning algorithms, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR). The performance of the models was assessed using area under the receiver operating characteristic curve (AUC) values. Importance matrix plot and SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and to show the visualization results. The nomogram and clinical impact curve were used to validate the final model.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Two hundred and seventeen children with COVID-19 had seizures. According to the AUC, the RF model performed the best. Based on the SHAP values, the top three most important variables in the RF model were neutrophil percentage, cough and fever duration. The nomogram and clinical impact curve also verified that the RF model possessed significant predictive value.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Our research indicates that the RF model demonstrates excellent performance in predicting seizures, and our novel nomogram can facilitate clinical decision-making and potentially offer benefit for clinicians to prevent and treat seizures in children with COVID-19.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/tropej\/fmae011","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T23:56:13Z","timestamp":1714175773000},"source":"Crossref","is-referenced-by-count":2,"title":["Development of a machine learning model and nomogram to predict seizures in children with COVID-19: a two-center 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