{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:46:02Z","timestamp":1786117562073,"version":"build-2736575974"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":30,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371429"],"award-info":[{"award-number":["42371429"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"COVID-19 Pandemic on Child Growth and Development in China\u201d)"},{"name":"the Public Service Development and Reform Pilot"},{"name":"the Capital\u2019s Funds for Health Improvement and Research","award":["2024-2-1133"],"award-info":[{"award-number":["2024-2-1133"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Backgrounds and Objectives<\/jats:title>\n                    <jats:p>Childhood obesity and respiratory tract infections (RTIs) are 2 major global public health issues that frequently co-occur and are closely interrelated. Early detection of children with prior RTIs who are at high obesity risk is crucial for targeted interventions. This study integrates interpretable machine learning (ML) models and a deep learning network to develop an obesity risk prediction model in a large pediatric cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Cross-sectional data from 6509 children and adolescents aged 3-12 years with prior RTIs in Beijing and Tangshan were fed to 12 ML models to predict childhood obesity (versus children with normal weight). Bayesian optimization was applied to fine-tune model hyperparameters. Prediction performance was assessed using 8 metrics. Key predictive features were identified by SHapley Additive exPlanations (SHAP). The validity of the optimal ML model was verified by the sequential neural network model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 12 ML models, LightGBM achieved the optimal performance (accuracy: 0.8844, area under the curve [AUC]: 0.9491). SHAP analysis identified 20 key predictors, including child age, paternal body mass index (BMI), maternal BMI, birth length, birthweight, gestational age, eating speed, complementary feeding initiation age, screen time, breastfeeding duration, bedtime, maternal age, nighttime sleep, outdoor activity, dental caries, sedentary time, food allergies, family history of diabetes, and sex. The deep learning sequence network model further validated the predictive value of these features (accuracy: 0.8023, AUC: 0.8117), and the SHAP-driven feature importance rankings were in close line with LightGBM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our LightGBM-based model enables effective prediction of obesity risk in children aged 3-12 years with prior RTIs, and the key features identified can inform early screening and facilitate personalized interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag061","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:31:21Z","timestamp":1775561481000},"source":"Crossref","is-referenced-by-count":0,"title":["Identification of obesity risk factors in 3-12-year-old children and adolescents with prior respiratory tract infections via interpretable machine and deep learning models"],"prefix":"10.1093","volume":"9","author":[{"given":"Xiao-Qian","family":"Wang","sequence":"first","affiliation":[{"name":"Capital Institute of Pediatrics, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100020,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang-Jie-Yi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Center for Evidence-Based Medicine, Capital Center for Children\u2019s Health, Capital Medical University, Capital Institute of Pediatrics , Beijing 100020,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiong","family":"Wang","sequence":"additional","affiliation":[{"name":"Graduate School, Beijing University of Chinese Medicine , Beijing 100029,","place":["China"]},{"name":"Department of Pediatrics, China-Japan Friendship Hospital , Beijing 100029,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Che","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Evidence-Based Medicine, Capital Center for Children\u2019s Health, Capital Medical University, Capital Institute of Pediatrics , Beijing 100020,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Evidence-Based Medicine, Capital Center for Children\u2019s Health, Capital Medical University, Capital Institute of Pediatrics , Beijing 100020,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, China-Japan Friendship Hospital , Beijing 100029,","place":["China"]},{"name":"Institute of Clinical Medical Sciences, China-Japan Friendship Hospital , Beijing 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