[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127931-en":3,"doc-seo-127931-105":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},127931,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting adverse drug event using machine learning based on electronic health records - a systematic review and meta-analysis","Adverse drug events (ADEs) create major clinical risks, and machine learning increasingly supports prediction using electronic health record (EHR) data. This systematic review summarizes how ML models forecast specific ADEs from EHRs, based on searches across PubMed, Web of Science, Embase, and IEEEXplore. Fifty-nine studies were included, spanning 15 drugs and 15 ADEs, reporting 38 algorithms with commonly used random forest, SVM, and boosting methods, and generally strong classification performance.","TYPE Systematic Review PUBLISHED 13 November 2024 DOI 10.3389/fphar.2024.1497397  \nOPEN ACCESS  \nEDITED BY  \nShusen Sun,  \nWestern New England University, United States  \nREVIEWED BY  \nSheraz Ali,  \nUniversity of Tasmania, Australia Ziran Li,  \nUniversity of California, San Francisco, United States  \n*CORRESPONDENCE  \nZhiyao He,  \n [zhiyaohe@scu.edu.cn](zhiyaohe@scu.edu.cn)[ ](zhiyaohe@scu.edu.cn)Ting Xu,  \n [tingx2009@163.com](tingx2009@163.com)  \nRECEIVED 17 September 2024  \nACCEPTED 21 October 2024  \nPUBLISHED 13 November 2024  \nCITATION  \nHu Q, Chen Y, Zou D, He Z and Xu T (2024) Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.  \nFront. Pharmacol. 15:1497397 .  \ndoi: 10.3389/fphar.2024.1497397  \nCOPYRIGHT  \n© 2024 Hu, Chen, Zou, He and Xu. 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.  \nPredicting adverse drug event using machine learning based on electronic health records: a systematic review and  \nmeta-analysis  \nQiaozhi Hu 1,2, Yuxian Chen 1, Dan Zou 1, Zhiyao He 1,3* and Ting Xu 1*  \n1Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, Sichuan, China, 2West China School of Medicine, Sichuan University, Chengdu, Sichuan, China, 3Key Laboratory of DrugTargeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for PlantSourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China  \nIntroduction: Adverse drug events (ADEs) pose a significant challenge in current clinical practice. Machine learning (ML) has been increasingly used to predict specific ADEs using electronic health record (EHR) data. This systematic review provides a comprehensive overview of the application of ML in predicting specific ADEs based on EHR data.  \nMethods: A systematic search of PubMed, Web of Science, Embase, and IEEEXplore was conducted to identify relevant articles published from the inception to 20 May 2024 . Studies that developed ML models for predicting specific ADEs or ADEs associated with particular drugs were included using EHR data.  \nResults: A total of 59 studies met the inclusion criteria, covering 15 drugs and 15 ADEs. In total, 38 machine learning algorithms were reported, with random forest (RF) being the most frequently used, followed by support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), and light gradient boosting machine (LightGBM) . The performance of the ML models was generally strong, with an average area under the curve (AUC) of 76.68% ± 10.73, accuracy of 76.00% ± 11.26, precision of 60.13% ± 24.81, sensitivity of 62.35% ± 20.19, specificity of 75.13% ± 16 .60, and an F1 score of 52 .60% ± 21 .10. The combined sensitivity, specificity, diagnostic odds ratio (DOR), and AUC from the summary receiver operating characteristic (SROC) curve using a random effects model were 0.65 (95% CI: 0.65–0.66), 0.89 (95% CI: 0.89–0.90), 12.11 (95% CI: 8.17–17.95), and 0.8069, respectively. The risk factors associated with different drugs and ADEs varied.  \nDiscussion: Future research should focus on improving standardization, conducting multicenter studies that incorporate diverse data types, and evaluating the impact of artificial intelligence predictive models in real-world clinical settings.  \nSystematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_](https://www.crd.york.ac.uk/prospero/display_)[record.php?ID=CRD42024565842](record.php?ID=CRD42024565842), [identi](","cbCaidMqm4EwxuW6","https://ap.wps.com/l/cbCaidMqm4EwxuW6","pdf",2290001,4,1,17,"English","en",105,"# Introduction\n## Methods\n## Results\n## Discussion","[{\"question\":\"What does the systematic review focus on?\",\"answer\":\"The review focuses on applying machine learning to predict specific adverse drug events using electronic health record data.\"},{\"question\":\"Which studies and data sources were included?\",\"answer\":\"Studies developing ML models for predicting specific ADEs or ADEs linked to particular drugs were included, identified via searches in PubMed, Web of Science, Embase, and IEEEXplore through 20 May 2024.\"},{\"question\":\"What were the key findings on model performance and algorithms?\",\"answer\":\"Fifty-nine studies met criteria, reporting 38 ML algorithms; random forest was most frequently used. Reported performance showed generally strong discrimination with an average AUC of 76.68% ± 10.73 and related summary metrics from SROC analysis.\"}]","Predicting adverse drug event using machine learning based on electronic health records - a systematic review and meta-analysis | PDF",1785943070,43,{"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},"predicting-adverse-drug-event-using-machine-learning-based-on-electronic-health-records-a-systematic-review-and-meta-analysis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/predicting-adverse-drug-event-using-machine-learning-based-on-electronic-health-records-a-systematic-review-and-meta-analysis/127931/",{"url":53,"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","2026-08-26","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 does the systematic review focus on?","Question",{"text":76,"@type":77},"The review focuses on applying machine learning to predict specific adverse drug events using electronic health record data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which studies and data sources were included?",{"text":81,"@type":77},"Studies developing ML models for predicting specific ADEs or ADEs linked to particular drugs were included, identified via searches in PubMed, Web of Science, Embase, and IEEEXplore through 20 May 2024.",{"name":83,"@type":74,"acceptedAnswer":84},"What were the key findings on model performance and algorithms?",{"text":85,"@type":77},"Fifty-nine studies met criteria, reporting 38 ML algorithms; random forest was most frequently used. 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