[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128022-en":3,"doc-seo-128022-105":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},128022,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment - Development of an Automated Machine Learning Model","Drug-induced liver injury (DILI) is a frequent and serious adverse reaction during pediatric tuberculosis therapy, especially with first-line anti-tuberculosis drugs. This study develops an automatic machine learning (AutoML) model to predict anti-tuberculosis drug-induced liver injury (ATB-DILI) risk in children using retrospective clinical data and therapeutic drug monitoring results. Features were screened via univariate risk factor analysis, model performance was assessed with ROC AUC, and variable contributions were interpreted using TreeShap, supporting timely clinician decision-making and mitigation strategies.","Drug Design, Development and Therapy downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nDrug Design, Development and Therapy  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nRisk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model  \nYing Zeng 1 , *, Hong Lu 1 , *, Sen Li 2 , *, Qun-Zhi Shi 1 , Lin Liu 1 , Yong-Qing Gong 1 , Pan Yan 1  \n1Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, 410004, People’s Republic of China; 2Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China  \n*These authors contributed equally to this work  \nCorrespondence: Pan Yan, Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, People’s Republic of China, Email [2022050025@usc.edu.cn](2022050025@usc.edu.cn)  \n\n| Purpose: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line antituberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children.\u003Cbr>Methods: A retrospective study was performed on the clinical data and therapeutic drug monitoring (TDM) results of children initially treated for tuberculosis at the affiliated Changsha Central Hospital of University of South China. After the features were screened by univariate risk factor analysis, AutoML technology was used to establish predictive models. The area under the receiver operating characteristic curve (AUC) was used to evaluate model’s performance, and then the TreeShap algorithm was employed to interpret the variable contributions.\u003Cbr>Results: A total of 184 children were enrolled in this study, of whom 19 (10.33%) developed ATB-DILI. Univariate analysis showed that seven variables were risk factors for ATB-DILI, including the plasma peak concentration (Cmax) of rifampicin, body mass index (BMI), alanine aminotransferase, total bilirubin, total bile acids, aspartate aminotransferase and creatinine. Among the numerous predictive models constructed by the “H2O” AutoML platform, the gradient boost machine (GBM) model exhibited the superior performance with AUCs of 0.838 and 0.784 on the training and testing sets, respectively. The TreeShap algorithm showed that Cmax of rifampicin and BMI were important features that affect the AutoML model’s performance.\u003Cbr>Conclusion: The GBM model established by AutoML technology shows high predictive accuracy and interpretability for ATB-DILI in children. The prediction model can assist clinicians to implement timely interventions and mitigation strategies, and formulate personalized medication regimens, thereby minimizing potential harm to high-risk children of ATB-DILI.\u003Cbr>Keywords: anti-tuberculosis drug-induced liver injury, children, retrospective study, automatic machine learning, gradient boost machine |\n| --- |\n| Introduction\u003Cbr>Tuberculosis (TB), a chronic infectious disease caused by Mycobacterium tuberculosis, is a major global public health challenge and has become the leading cause of death among single infectious diseases.1 The currently accepted first-line anti-TB drugs, including isoniazid, rifampin, pyrazinamide and ethambutol, play a central role in TB treatment and, when used in combination, can improve cure rates and reduce the development of drug resistance.2–4 However, antituberculosis drug-induced liver injury (ATB-DILI) has become the most common and serious adverse effect of TB treatment.5 ATB-DILI can cause patients to discontinue treatment with single or combination anti-TB drugs, and discontinuation and rechallenge of anti-TB drugs can lead to the emergence of ","cbCaihQcSqlVfHBT","https://ap.wps.com/l/cbCaihQcSqlVfHBT","pdf",3045431,2,1,12,"English","en",105,"# Purpose\n# Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What is the purpose of this study?\",\"answer\":\"To develop an AutoML model that predicts the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children undergoing pediatric tuberculosis treatment.\"},{\"question\":\"How were the predictive models built and evaluated?\",\"answer\":\"The study used retrospective clinical data and therapeutic drug monitoring (TDM) results; features were screened by univariate risk factor analysis, and model performance was evaluated using ROC AUC. TreeShap was then used for interpretability.\"},{\"question\":\"Which model performed best and what key features influenced it?\",\"answer\":\"The gradient boost machine (GBM) model showed superior performance with AUCs of 0.838 (training) and 0.784 (testing). TreeShap indicated that rifampicin Cmax and body mass index (BMI) were important contributing features.\"}]","Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment - Development of an Automated Machine Learning Model | PDF",1785944015,30,{"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},"risk-prediction-of-liver-injury-in-pediatric-tuberculosis-treatment-development-of-an-automated-machine-learning-model","",{"@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/risk-prediction-of-liver-injury-in-pediatric-tuberculosis-treatment-development-of-an-automated-machine-learning-model/128022/",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","2026-08-27","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 is the purpose of this study?","Question",{"text":76,"@type":77},"To develop an AutoML model that predicts the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children undergoing pediatric tuberculosis treatment.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the predictive models built and evaluated?",{"text":81,"@type":77},"The study used retrospective clinical data and therapeutic drug monitoring (TDM) results; features were screened by univariate risk factor analysis, and model performance was evaluated using ROC AUC. TreeShap was then used for interpretability.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best and what key features influenced it?",{"text":85,"@type":77},"The gradient boost machine (GBM) model showed superior performance with AUCs of 0.838 (training) and 0.784 (testing). TreeShap indicated that rifampicin Cmax and body mass index (BMI) were important contributing features.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":30,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]