{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T20:13:24Z","timestamp":1792959204140,"version":"4.5.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"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. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early manifestations of pediatric Central Nervous System Infection (CNSI) lack specificity and are difficult to distinguish from Febrile Seizures (FS). Previous machine learning research has primarily focused on general infection risk stratification, with few studies on discriminative models for pediatric CNSI using initial clinical data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To construct an exploratory machine learning-based model for early risk stratification to differentiate pediatric CNSI from FS using single-center retrospective data and to analyze key predictive factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Children hospitalized with fever and convulsions between January 2023 and December 2025 were enrolled. Initial clinical features and laboratory test results were used for univariate screening. After multicollinearity handling (Spearman correlation, |r|\u2009\u2265\u20090.70) and feature importance ranking via Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms, a union set of 10 variables was selected for modeling. The performance of multiple algorithms was compared based on the Area Under the Receiver Operating Characteristic Curve (AUC). Model interpretability was assessed using a nomogram and Shapley Additive exPlanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 140 children were included (28 in the CNSI group, 112 in the FS group). 10 core features were selected, including: Calcium (Ca) concentration, Lymphocyte Percentage (L%), Serum Albumin (ALB) level, Red Blood Cell (RBC) count, Oxygen Saturation (SO\n                      <jats:sub>2<\/jats:sub>\n                      ), Lactic Acid (LAC), Neutrophil Percentage (\n                      <jats:italic>N<\/jats:italic>\n                      %), Babkinki sign, Headache, and Electroencephalogram (EEG) findings. Among the algorithms, Support Vector Machine (SVM) achieved a relatively high AUC of 0.864 (95% CI: 0.625\u20131.000) in the internal test set. SHAP analysis indicated that Ca, SO\n                      <jats:sub>2<\/jats:sub>\n                      , and L% contributed significantly to the model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed an exploratory discriminative model for differentiating pediatric CNSI from FS using variables available at initial diagnosis. The model provides preliminary insights for early risk stratification of pediatric CNSI, though its generalizability and clinical utility require further validation through multicenter prospective external studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2026.1861527","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T05:44:22Z","timestamp":1784267062000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A predictive model for central nervous system infections in children based on machine learning and clinical diagnostic features"],"prefix":"10.3389","volume":"14","author":[{"given":"Bin","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Infectious Diseases, Children\u2019s Hospital of Fudan University (Xiamen Branch), Xiamen Children\u2019s Hospital","place":["Xiamen, Fujian, 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