[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128121-en":3,"doc-seo-128121-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128121,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Clinical prediction model by machine learning to determine the results of maternal dietary avoidance in food protein-induced allergic proctocolitis infants - Research","A retrospective cohort study investigates determinants of outcomes after maternal dietary avoidance in infants diagnosed with food protein-induced allergic proctocolitis (FPIAP). Using Lasso regression for variable selection, multiple machine learning classifiers were trained, and the most stable approach was chosen for prediction. Among 693 children, remission rate with avoidance was 47.38%, with hypoallergenic formula efficacy at 88.48%. The final logistic model reached AUC 0.743 and accuracy 0.699, with calibration showing acceptable fit and visualization via a nomogram.","TYPE Original Research PUBLISHED 16 May 2025  \nDOI 10.3389/fped.2025.1612076  \nEDITED BY  \nConsolato M. Sergi,  \nChildren’s Hospital of Eastern Ontario (CHEO), Canada  \nREVIEWED BY  \nJeferson Aloísio Ströher,  \nFederal University of Rio Grande do Sul, Brazil Daniela De Castro Barbosa Leonello, University of São Paulo, Brazil  \nMarina Mayumi Vendrame Takao, State University of Campinas, Brazil  \n*CORRESPONDENCE  \nLi-jing Xiong  \n [xionglijing1985@uestc.edu.cn](xionglijing1985@uestc.edu.cn)[ ](xionglijing1985@uestc.edu.cn)RECEIVED 15 April 2025  \nACCEPTED 05 May 2025  \nPUBLISHED 16 May 2025  \nCITATION  \nLi J, Zhou M-y, Li Y, Wu X, Li X, Xie X-l and Xiong L-j (2025) Clinical prediction model by machine learning to determine the results of maternal dietary avoidance in food proteininduced allergic proctocolitis infants.  \nFront. Pediatr. 13:1612076 .  \ndoi: 10.3389/fped.2025.1612076  \nCOPYRIGHT  \n© 2025 Li, Zhou, Li, Wu, Li, Xie and Xiong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nClinical prediction model by machine learning to determine the results of maternal dietary avoidance in food protein-induced allergic proctocolitis infants  \nJing Li, Meng-yao Zhou, Yang Li, Xue Wu, Xin Li, Xiao-li Xie and Li-jing Xiong*  \nDepartment of Pediatric Gastroenterology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China  \nObjective: The objective of this study was to investigate the factors associated with the results of maternal dietary avoidance in infants diagnosed with Food ProteinInduced Allergic Proctocolitis (FPIAP) . Additionally, we aimed to develop a predictive model using machine learning techniques to forecast the results of maternal dietary restrictions.  \nMethods: The clinical data ofFPIAP infants were retrospectively analyzed. The FPIAP infants were divided into two groups based on the results of maternal dietary restriction, and an analysis was conducted to identify the influencing factors. Variable was selected by Lasso regression model. Classification models were built utilizing various machine learning algorithms including XGB Classifier, Logistic Regression, Random Forest Classifier, Ada Boost Classifier, KNeighbors Classifier, LGBM Classifier, Decision Tree Classifier, Gradient Boosting Classifier, Support Vector Classifier. The optimal algorithm was selected to construct the final prediction model.  \nResults: In a retrospective cohort study of 693 children diagnosed with FPIAP, theremission rate associated with maternal dietary avoidance was 47 .38% . The overall efficacy of hypoallergenic formula was 88 .48% . Multivariate analysis identified several factors influencing the outcome of maternal dietary restriction, including age, disease duration, regurgitation, eczema, and neonatal history of hematochezia. Variables were selected and incorporated into multiple machine learning models. Among them, the logistic regression model demonstrated relatively high stability and was ultimately selected for modeling. The final model achieved an AUC of 0.743 in the test set and an accuracy of 0 .699. The validation set’s AUC was within 10% of the test set’s value, indicating acceptable generalizability. The HosmerLemeshow goodness-of-fit test confirmed that the logistic regression model fit the data well (P = 0 .691 > 0 . 05) . Finally, a nomogram was used to visualize the model’s performance, and the Brier Score in the calibration curve was 0 .210.  \nConclusion: This study provided a predictive model for formulating individualized diagnostic strategies of sus","cbCaia0DRhrRbQCe","https://ap.wps.com/l/cbCaia0DRhrRbQCe","pdf",1537993,2,1,10,"English","en",105,"# Objective\n# Methods\n## Study design and data\n## Variable selection and machine learning models\n## Model selection and evaluation\n# Results\n## Remission and treatment efficacy\n## Influencing factors\n## Model performance metrics\n## Calibration and nomogram\n# Conclusion\n# References","[{\"question\":\"What was the main objective of the study on maternal dietary avoidance in FPIAP infants?\",\"answer\":\"To identify factors associated with outcomes of maternal dietary avoidance and to develop a machine learning predictive model for forecasting those outcomes.\"},{\"question\":\"How were variables selected for building the prediction models?\",\"answer\":\"Variables were selected using the Lasso regression model, then incorporated into multiple machine learning classifiers.\"},{\"question\":\"Which model was selected as the final predictive model, and how well did it perform?\",\"answer\":\"Logistic regression was selected for its relative stability. It achieved an AUC of 0.743 and accuracy of 0.699 in the test set, with the validation AUC within 10% of the test set.\"}]","Clinical prediction model by machine learning to determine the results of maternal dietary avoidance in food protein-induced allergic proctocolitis infants - Research | PDF",1785944938,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"clinical-prediction-model-by-machine-learning-to-determine-the-results-of-maternal-dietary-avoidance-in-food-protein-induced-allergic-proctocolitis-infants-research","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/clinical-prediction-model-by-machine-learning-to-determine-the-results-of-maternal-dietary-avoidance-in-food-protein-induced-allergic-proctocolitis-infants-research/128121/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What was the main objective of the study on maternal dietary avoidance in FPIAP infants?","Question",{"text":76,"@type":77},"To identify factors associated with outcomes of maternal dietary avoidance and to develop a machine learning predictive model for forecasting those outcomes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were variables selected for building the prediction models?",{"text":81,"@type":77},"Variables were selected using the Lasso regression model, then incorporated into multiple machine learning classifiers.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model was selected as the final predictive model, and how well did it perform?",{"text":85,"@type":77},"Logistic regression was selected for its relative stability. 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