{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T07:56:49Z","timestamp":1791446209571,"version":"4.3.3"},"reference-count":19,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Allergy Immunology"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Food sensitization (FS) is a common comorbidity in children with atopic dermatitis (AD), often leading to the atopic march. Identifying high\u2010risk patients in primary care is crucial to prioritize specialist referrals and prevent unnecessary elimination diets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate a machine learning (ML) based clinical decision support tool to predict IgE\u2010mediated FS risk in infants and toddlers (0\u20133\u2009years) diagnosed with AD using routine clinical and laboratory parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective cohort study of children diagnosed with AD. Patients were classified as FS positive or FS negative based on specific IgE levels and skin prick tests. Ten ML algorithms were trained and compared using 5\u2010fold stratified cross\u2010validation. Feature importance was analyzed, and an interactive web\u2010based tool was developed for clinical application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 340 patients, 156 (45.8%) had IgE\u2010mediated FS. Key independent predictors of FS included higher total IgE levels, younger age at presentation, elevated eosinophil counts, and breastfeeding at the time of symptom onset. Boosting\u2010based models achieved superior performance; specifically, the AdaBoost model demonstrated high stability and discrimination (ROC AUC: 0.815 in CV and 0.819 in the held\u2010out test set). By optimizing the probability threshold to 0.40, the model achieved a recall of 92% and an F1\u2010score of 0.75, effectively minimizing false negatives.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The ML model may serve as a screening and risk\u2010stratification tool for IgE\u2010mediated FS in children with AD. By utilizing routine laboratory data, this web\u2010integrated tool can support primary care physicians in risk stratification, ensuring timely referral for high\u2010risk cases while reducing unnecessary testing in low\u2010risk infants.<\/jats:p>\n                    <jats:p>\n                      <jats:boxed-text content-type=\"graphic\" position=\"anchor\">\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/pai70518-gra-0001-m.png\">\n                          <jats:alt-text>image<\/jats:alt-text>\n                        <\/jats:graphic>\n                      <\/jats:boxed-text>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/pai.70518","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T07:46:14Z","timestamp":1791445574000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning\u2010based prediction of food sensitization in infants and toddlers with atopic dermatitis"],"prefix":"10.1111","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2675-1150","authenticated-orcid":false,"given":"R\u0131za","family":"Y\u0131ld\u0131r\u0131m","sequence":"first","affiliation":[{"name":"Division of Pediatric Allergy and Immunology, Department of Pediatrics Izmir City Hospital  Izmir Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6927-1261","authenticated-orcid":false,"given":"Ece","family":"\u015eenbaykal Yi\u011fit","sequence":"additional","affiliation":[{"name":"Division of Pediatric Allergy and 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Immunology, Department of Pediatrics Izmir City Hospital  Izmir Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3193-1062","authenticated-orcid":false,"given":"Seda","family":"Tunca","sequence":"additional","affiliation":[{"name":"Division of Pediatric Allergy and Immunology, Department of Pediatrics Izmir City Hospital  Izmir Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2170-765X","authenticated-orcid":false,"given":"Sanem","family":"Eren Akarcan","sequence":"additional","affiliation":[{"name":"Division of Pediatric Allergy and Immunology, Department of Pediatrics Izmir City Hospital  Izmir Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0965-0726","authenticated-orcid":false,"given":"\u0130dil","family":"Akay Hac\u0131","sequence":"additional","affiliation":[{"name":"Division of Pediatric Allergy and Immunology, 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