{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T13:23:52Z","timestamp":1766150632969,"version":"3.48.0"},"reference-count":53,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:00:00Z","timestamp":1746403200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82400592"],"award-info":[{"award-number":["82400592"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82300574"],"award-info":[{"award-number":["82300574"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Investigation"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Importance<\/jats:title>\n                    <jats:p>Early diagnosis of biliary atresia (BA) is important for advancing the Kasai operation time and improving the BA prognosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop machine learning (ML) models for neonatal BA diagnosis using clinical characteristics and serological data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Neonates presenting with pathological jaundice between January 1, 2013, and December 31, 2023 were enrolled. Five ML models\u2014logistic regression (LR), random forest (RF), support vector machine classifier (SVC), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)\u2014were trained using neonatal clinical and laboratory data. The stacking classifier (SC) algorithm was employed to select the best\u2010performing models for constructing the ensemble learning model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This study included 85 patients, 42 of whom were diagnosed with BA. Among the five ML models, XGBoost (area under the receiver operating characteristic curve [AUC] = 1.000; 95% confidence interval [CI]: 1.000\u20131.000) and the RF (AUC = 1.000; 95% CI: 1.000\u20131.000) demonstrated better diagnostic performance. All models showed acceptable consistency between the predicted and actual probabilities. The SC model, built on the LR, RF, and XGBoost models, also exhibited a strong generalization ability and diagnostic performance (AUC = 1.000; 95% CI: 1.000\u20131.000). Key diagnostic predictors included elevated gamma\u2010glutamyl transpeptidase (GGT) (AUC = 0.837; 95% CI: 0.749\u20130.925), increased platelet (PLT) counts (AUC = 0.728; 95% CI: 0.618\u20130.838), and acholic stools (AUC = 0.765; 95% CI: 0.663\u20130.867). An XGBoost\u2010based nomogram was also developed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>ML models demonstrate high diagnostic accuracy for BA in neonates, with GGT, PLT counts, and acholic stools as pivotal predictors. This approach may enable earlier BA identification and intervention during the neonatal period.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ped4.70009","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T05:33:34Z","timestamp":1746423214000},"page":"361-371","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of machine learning in constructing a diagnostic model for neonatal biliary atresia"],"prefix":"10.1002","volume":"9","author":[{"given":"Dingding","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Neonatal Surgery Beijing Children's Hospital Capital Medical University National Center for Children's Health  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Neonatal Surgery Beijing Children's Hospital Capital Medical 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