{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:04:49Z","timestamp":1784613889417,"version":"3.55.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"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>\n                      To construct and validate an interpretable machine learning model for early prediction of severe\n                      <jats:italic>Mycoplasma pneumoniae<\/jats:italic>\n                      pneumonia (SMPP) in children.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Clinical and immunological data of 402 pediatric MPP patients were randomly divided into a training set (70% for model tuning) and a held-out internal test set (30% for final evaluation). We systematically evaluated 113 algorithms based on data collected within 24\u202fh of admission.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The Gradient Boosting Machine (GBM) demonstrated optimal performance, achieving an AUC of 0.805 (95% CI: 0.724\u20130.886) on the held-out internal test set. The model identified 16 core predictors: dyspnea, C-reactive protein (CRP), total T lymphocytes (CD3+), sputum plug formation (PB), CD3+CD4+CD8\u2212 T cells, CD3+CD56+NKT cells, LDH, IL-6, creatine kinase (CK), PCT, ALT, IgA, abnormal coagulation function (D-dimer), CK-MB, erythrocyte sedimentation rate (ESR), and MP-DNA load. SHapley Additive exPlanations (SHAP) analysis revealed that dyspnea, elevated CRP, and decreased T lymphocytes synergistically drive severe illness risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The GBM-based model effectively predicts SMPP risk. Pending prospective multi-center validation, we plan to embed this transparent, data-driven tool into electronic medical record (EMR) systems to provide real-time early warning and individualized risk assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1843690","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T05:34:20Z","timestamp":1784612060000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-driven risk assessment of severe Mycoplasma pneumoniae in children: analysis based on core clinical and immunological features"],"prefix":"10.3389","volume":"13","author":[{"given":"Duoduo","family":"Li","sequence":"first","affiliation":[{"name":"Department of Pediatrics, The First Affiliated Hospital of Henan Medical University","place":["Weihui, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, The First Affiliated Hospital of Henan Medical University","place":["Weihui, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Nephrology, The First Affiliated Hospital of Henan Medical University","place":["Weihui, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luyang","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, The First Affiliated Hospital of Henan Medical University","place":["Weihui, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yishuai","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, The First Affiliated Hospital of Henan Medical University","place":["Weihui, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xixia","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, The First Affiliated Hospital of Henan Medical University","place":["Weihui, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihong","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, The First Affiliated Hospital of Henan Medical University","place":["Weihui, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangtao","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, The First Affiliated Hospital of Henan Medical University","place":["Weihui, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fenglian","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, The First Affiliated Hospital of 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