[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127644-en":3,"doc-seo-127644-105":31,"detail-sidebar-cat-0-en-105":96},{"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},127644,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Comparative effectiveness of explainable machine learning approaches - Extrauterine growth restriction classification in preterm infants using longitudinal data","Preterm birth remains a major cause of infant mortality and morbidity, and insufficient postnatal growth (extrauterine growth restriction, EUGR) presents ongoing clinical difficulty. This study develops an automated, interpretable machine-learning framework for predicting short-term growth outcomes in preterm infants using longitudinal weight and length data. Four datasets were constructed for baseline and follow-up measures and evaluated with SVM and logistic regression using five-fold cross-validation. Model trustworthiness was assessed via calibration and cumulative curves, while SHAP provided global interpretability, highlighting key clinical variables and enabling reliable decision support.","TYPE Original Research PUBLISHED 29 November 2023 DOI 10.3389/fmed.2023.1166743  \nOPEN ACCESS  \nEDITED BY  \nShi Yuan,  \nChildren’s Hospital of Chongqing Medical University, China  \nREVIEWED BY  \nWonsang You,  \nSun Moon University, Republic of Korea Guang Yue,  \nChengdu Women’s and Children’s Central Hospital, China  \n*CORRESPONDENCE  \nHeui Seung Jo  \n [jo-zo@hanmail.net](jo-zo@hanmail.net)[ ](jo-zo@hanmail.net)Payam Hosseinzadeh Kasani  \n [payam.kassani@kangwon.ac.kr](payam.kassani@kangwon.ac.kr)[ ](payam.kassani@kangwon.ac.kr)RECEIVED 15 February 2023 ACCEPTED 07 November 2023 PUBLISHED 29 November 2023  \nCITATION  \nCho KH, Kim ES, Kim JW, Yun C-H, Jang J-W, Kasani PH and Jo HS (2023) Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data.  \nFront. Med. 10:1166743 .  \ndoi: 10.3389/fmed.2023.1166743  \nCOPYRIGHT  \n© 2023 Cho, Kim, Kim, Yun, Jang, Kasani and Jo. 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.  \nComparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data  \nKee Hyun Cho 1, 2, Eun Sun Kim 1, 2, Jong Wook Kim3, Cheol-Heui Yun4, 5, Jae-Won Jang 6, 7,  \nPayam Hosseinzadeh Kasani 6, 8* and Heui Seung Jo 1, 2*  \n1 Department of Pediatrics, Kangwon National University Hospital, Chuncheon, Republic of Korea,  \n2 Department of Pediatrics, Kangwon National University School of Medicine, Chuncheon, Republic of Korea, 3 Department of Computer Science, Sangmyung University, Seoul, Republic of Korea,  \n4 Department of Agricultural Biotechnology, Seoul National University, Seoul, Republic of Korea,  \n5 Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea, 6 Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea, 7 Department of Neurology, Kangwon National University School of Medicine, Chuncheon, Republic of Korea, 8 Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea  \nIntroduction: Preterm birth is a leading cause of infant mortality and morbidity. Despite the improvement in the overall mortality in premature infants, the intact survival of these infants remains a significant challenge. Screening the physical growth of infants is fundamental to potentially reducing the escalation of this disorder. Recently, machine learning models have been used to predict the growth restrictions of infants; however, they frequently rely on conventional risk factors and cross-sectional data and do not leverage the longitudinal database associated with medical data from laboratory tests.  \nMethods: This study aimed to present an automated interpretable ML-based approach for the prediction and classification of short-term growth outcomes in preterm infants. We prepared four datasets based on weight and length including weight baseline, length baseline, weight follow-up, and length follow-up. The CHA Bundang Medical Center Neonatal Intensive Care Unit dataset was classified using two well-known supervised machine learning algorithms, namely support vector machine (SVM) and logistic regression (LR) . A five-fold cross-validation, and several performance measures, including accuracy, precision, recall and F1-score were used to compare classifier performances. We further illustrated the models’ trustworthiness using calibration and cumulative curves. The visualized global interpretations u","cbCaii7LNxpOarVn","https://ap.wps.com/l/cbCaii7LNxpOarVn","pdf",4436658,2,1,24,"English","en",105,"# Introduction\n## Methods\n## Results\n## Discussion\n# Keywords","[{\"question\":\"What problem does the study address in preterm infants?\",\"answer\":\"It targets extrauterine growth restriction (EUGR), defined as insufficient growth during hospitalization, which can affect outcomes beyond discharge.\"},{\"question\":\"How were the datasets constructed for model development?\",\"answer\":\"Four datasets were created from weight and length measures, covering baseline and follow-up (weight baseline, length baseline, weight follow-up, length follow-up).\"},{\"question\":\"Which models were compared and how was performance evaluated?\",\"answer\":\"Support vector machine (SVM) and logistic regression (LR) were compared using five-fold cross-validation and metrics including accuracy, precision, recall, and F1-score, along with ROC/AUC discrimination.\"},{\"question\":\"How does the study ensure interpretability and trustworthiness?\",\"answer\":\"Trustworthiness was evaluated using calibration and cumulative curves, and SHAP was used to provide global interpretation of variable contributions to predictions.\"}]","Comparative effectiveness of explainable machine learning approaches - Extrauterine growth restriction classification in preterm infants using longitudinal data | PDF",1785940480,60,{"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":91,"head_meta":93,"extra_data":95,"updated_unix":29},"comparative-effectiveness-of-explainable-machine-learning-approaches-extrauterine-growth-restriction-classification-in-preterm-infants-using-longitudinal-data","",{"@graph":37,"@context":90},[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/comparative-effectiveness-of-explainable-machine-learning-approaches-extrauterine-growth-restriction-classification-in-preterm-infants-using-longitudinal-data/127644/",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",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address in preterm infants?","Question",{"text":76,"@type":77},"It targets extrauterine growth restriction (EUGR), defined as insufficient growth during hospitalization, which can affect outcomes beyond discharge.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the datasets constructed for model development?",{"text":81,"@type":77},"Four datasets were created from weight and length measures, covering baseline and follow-up (weight baseline, length baseline, weight follow-up, length follow-up).",{"name":83,"@type":74,"acceptedAnswer":84},"Which models were compared and how was performance evaluated?",{"text":85,"@type":77},"Support vector machine (SVM) and logistic regression (LR) were compared using five-fold cross-validation and metrics including accuracy, precision, recall, and F1-score, along with ROC/AUC discrimination.",{"name":87,"@type":74,"acceptedAnswer":88},"How does the study ensure interpretability and trustworthiness?",{"text":89,"@type":77},"Trustworthiness was evaluated using calibration and cumulative curves, and SHAP was used to provide global interpretation of variable contributions to predictions.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":97},[98,102,106,110,114,119,124,127,132,135,139],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":30,"slug":113},5,"Comic","comic",{"id":115,"doc_module":4,"doc_module_name":47,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":47,"category_name":141,"show_sort_weight":111,"slug":142},19,"General","general"]